Careers at Cerebraix

• C2H (6 months contractual)
• Please share the candidates with Enterprise Application Development experience.
• Must have: Python (with Django), JQuery, Pandas, AWS, Enterprise App Development, OOP principles. 
• Good to have : HTML/CSS, ORM, PostgreSQL, REST APIs, Modern dev tool.

• Strong proficiency in Python programming language with Django framework.
• Should have a strong understanding of object-oriented design principles. Experience in java will be an added advantage.
• Good coding / debugging/ clean and reusable code writing skills.
• Having a good understanding of experience with ORM frameworks such as Hibernate or JPA.
• Understand code security / authentication and authorization mechanisms.
• Experience with database-driven development using PostgreSQL database.
• Should have experience leading teams and providing technical guidance.
• Should have experience building enterprise applications.
• Solid understanding of RESTful API design and implementation.
• Experience in Bootstrap 4 and jQuery will be an added advantage.
• Experience in Human Resource Information Systems will be an added advantage.
• Familiarity with Microservices architecture and design patterns.
• Experience with modern application development tools and libraries

• Role Senior AI Technical Lead – Agentic AI & Generative AI
• Experience 15+ years overall; 5+ years Architecture; 3+ years hands-on GenAI / Agentic AI
• Primary Focus Production-scale Agentic AI, Generative AI, multi-agent architectures and enterprise AI platforms
• Cloud AWS / Azure / GCP – experience with at least one cloud platform is mandatory

1. Role Overview

• We are seeking a Senior AI Technical Lead to own an Agentic AI engineering POD and lead the design, development and deployment of enterprise-grade Generative AI and multi-agent solutions. The role will translate functional requirements into technical workstreams, drive delivery across engineering PODs, contribute hands-on code, establish AI engineering standards, and mentor development teams.

2. Key Responsibilities

• Own the Agentic AI POD and deliver identified features in collaboration with Data, UI/UX, Infrastructure and other
• PODs.
• Translate functional requirements into technical tasks, architecture decisions and executable delivery plans;
• drive the POD through implementation.
• Design and develop multi-agent solutions using Azure AI Foundry and Azure AI Agent Service.
• Build AI agents and implement agent orchestration, workflow management, memory and state-management
• components.
• Design and implement RAG solutions using Azure AI Search and vector stores.
• Develop prompt-engineering, tool-calling, function-calling and agent-collaboration frameworks.
• Implement observability, distributed tracing, evaluation and AgentOps capabilities.
• Develop Human-in-the-Loop workflows and agent recovery mechanisms.
• Contribute directly to code development, conduct code reviews and mentor development teams.
• Troubleshoot performance, response quality, hallucination, model/agent drift and orchestration issues.
• Define and implement AI governance, security and compliance controls.
• Lead development and deployment of production-grade Generative AI solutions.

3. Mandatory Requirements – Non-Negotiable

Candidates who do not meet ANY one of the following mandatory requirements should be rejected at screening

stage.

1. Experience: 15+ years overall professional experience AND 5+ years hands-on Architecture experience.
2. GenAI / Agentic AI : 3+ years hands-on development experience in Generative AI and/or Agentic AI.
3. Enterprise AI: Demonstrated development AND deployment of enterprise grade production GenAI solutions.
4. Python: Strong hands-on expertise in Python.
5. Azure AI Foundry: Hands-on project experience using Azure AI Foundry.
6. Azure OpenAI: Hands-on project experience integrating/deploying LLM solutions using Azure OpenAI.
7. Azure AI Search: Hands-on experience implementing RAG/search solutions using Azure AI Search.
8. Azure AI Agent Service: Hands-on experience with Azure AI Agent Service.
9. Multi-Agent Architecture: Production-scale multi-agent systems including orchestration, workflow, memory and state management.
10. AI Engineering Controls: Hands-on governance, security, observability, evaluation and Human-in-the-Loop capabilities.
11. Cloud: Hands-on exposure to AWS, Azure or GCP.
12. Education: MS/M.Tech in CS, Information Systems, Computer Engineering or closely relevant discipline, or equivalent relevant degree.

Evidence rule: mandatory skills must be demonstrable through actual employment/project experience. Skills

appearing only in a profile summary, keyword/skills section or certification list are not sufficient evidence. Failure on

any mandatory criterion = REJECT.

4. Required Skills

• AI / GenAI: Generative AI, LLM applications, Agentic AI, multi-agent systems, RAG, prompt engineering, tool calling and function calling.
• Microsoft AI Stack: Azure AI Foundry, Azure OpenAI, Azure AI Search, Azure AI Agent Service, Document Intelligence and Azure AI services.
• AI Frameworks: Semantic Kernel, LangGraph, LangChain, AutoGen, MCP; practical agent orchestration and collaboration.
• Agent Engineering: Agent building, orchestration, workflow management, memory, state management, recovery and Human-in-the-Loop.
• AI Quality & Operations: Observability, tracing, evaluation, AgentOps, hallucination mitigation, performance/quality troubleshooting and drift management.
• Architecture: REST APIs, microservices, event-driven architecture, distributed systems and scalable enterprise architecture.
• Data & Storage: PostgreSQL, Cosmos DB, Redis, vector stores and enterprise retrieval architectures.
• Cloud & Deployment: Azure strongly preferred; AWS/GCP acceptable for the cloud requirement. Production AI deployment is essential.
• Engineering Practices: Code development, code reviews, technical documentation, Agile delivery and mentoring.
• Governance & Security: AI governance, security, compliance, responsible AI controls and enterprise deployment standards.

5. Qualifications

• Postgraduate degree (MS/M.Tech) in Computer Science, Information Systems, Computer Engineering or closely relevant discipline, or equivalent relevant qualification.
• 15+ years overall professional experience.
• 5+ years Architecture experience.
• 3+ years hands-on Generative AI / Agentic AI development.

6. Ideal Candidate Profile

• The ideal candidate is a hands-on AI architect/technical lead combining deep software architecture capability with recent production GenAI and Agentic AI engineering. They should be able to move from functional requirements to technical architecture, guide an engineering POD, make pragmatic platform choices, and personally contribute to implementation while ensuring enterprise-grade security, governance, observability and AI quality.

• Role Senior AI Lead Agentic & Gen AI - Azure AI Foundry
• Experience 15+ years overall; 5+ years Architecture; 3-5+ years MANDATORY AZURE AI FOUNDRY, 3+ years hands-on GenAI / Agentic AI
• Primary Focus Production-scale Agentic AI, Generative AI, multi-agent architectures and enterprise AI platforms
• Cloud AWS / Azure / GCP – experience with at least one cloud platform is mandatory

1. Role Overview

• We are seeking a Senior AI Lead - AZURE DATA FOUNDRY to own an Agentic AI engineering POD and lead the design, development and deployment of enterprise-grade Generative AI and multi-agent solutions. The role will translate functional requirements into technical workstreams, drive delivery across engineering PODs, contribute hands-on code, establish AI engineering standards, and mentor development teams.

2. Key Responsibilities

• Own the Agentic AI POD and deliver identified features in collaboration with Data, UI/UX, Infrastructure and other
• PODs.
• Translate functional requirements into technical tasks, architecture decisions and executable delivery plans;
• drive the POD through implementation.
• Design and develop multi-agent solutions using Azure AI Foundry and Azure AI Agent Service.
• Build AI agents and implement agent orchestration, workflow management, memory and state-management
• components.
• Design and implement RAG solutions using Azure AI Search and vector stores.
• Develop prompt-engineering, tool-calling, function-calling and agent-collaboration frameworks.
• Implement observability, distributed tracing, evaluation and AgentOps capabilities.
• Develop Human-in-the-Loop workflows and agent recovery mechanisms.
• Contribute directly to code development, conduct code reviews and mentor development teams.
• Troubleshoot performance, response quality, hallucination, model/agent drift and orchestration issues.
• Define and implement AI governance, security and compliance controls.
• Lead development and deployment of production-grade Generative AI solutions.

3. Mandatory Requirements – Non-Negotiable

Candidates who do not meet ANY one of the following mandatory requirements should be rejected at screening

stage.

1. Experience: 15+ years overall professional experience AND 5+ years hands-on Architecture experience.
2. GenAI / Agentic AI : 3+ years hands-on development experience in Generative AI and/or Agentic AI.
3. Enterprise AI: Demonstrated development AND deployment of enterprise grade production GenAI solutions.
4. Python: Strong hands-on expertise in Python.
5. Azure AI Foundry: Hands-on project experience using Azure AI Foundry.
6. Azure OpenAI: Hands-on project experience integrating/deploying LLM solutions using Azure OpenAI.
7. Azure AI Search: Hands-on experience implementing RAG/search solutions using Azure AI Search.
8. Azure AI Agent Service: Hands-on experience with Azure AI Agent Service.
9. Multi-Agent Architecture: Production-scale multi-agent systems including orchestration, workflow, memory and state management.
10. AI Engineering Controls: Hands-on governance, security, observability, evaluation and Human-in-the-Loop capabilities.
11. Cloud: Hands-on exposure to AWS, Azure or GCP.
12. Education: MS/M.Tech in CS, Information Systems, Computer Engineering or closely relevant discipline, or equivalent relevant degree.

Evidence rule: mandatory skills must be demonstrable through actual employment/project experience. Skills

appearing only in a profile summary, keyword/skills section or certification list are not sufficient evidence. Failure on

any mandatory criterion = REJECT.

4. Required Skills

• AI / GenAI (MUST HAVE): Generative AI, LLM applications, Agentic AI, multi-agent systems, RAG, prompt engineering, tool calling and function calling.
• Microsoft AI Stack (MUST HAVE): Azure AI Foundry, Azure OpenAI, Azure AI Search, Azure AI Agent Service, Document Intelligence and Azure AI services.
• AI Frameworks: Semantic Kernel, LangGraph, LangChain, AutoGen, MCP; practical agent orchestration and collaboration.
• Agent Engineering: Agent building, orchestration, workflow management, memory, state management, recovery and Human-in-the-Loop.
• AI Quality & Operations: Observability, tracing, evaluation, AgentOps, hallucination mitigation, performance/quality troubleshooting and drift management.
• Architecture: REST APIs, microservices, event-driven architecture, distributed systems and scalable enterprise architecture.
• Data & Storage: PostgreSQL, Cosmos DB, Redis, vector stores and enterprise retrieval architectures.
• Cloud & Deployment: Azure strongly preferred; AWS/GCP acceptable for the cloud requirement. Production AI deployment is essential.
• Engineering Practices: Code development, code reviews, technical documentation, Agile delivery and mentoring.
• Governance & Security: AI governance, security, compliance, responsible AI controls and enterprise deployment standards.

5. Qualifications

• Postgraduate degree (MS/M.Tech) in Computer Science, Information Systems, Computer Engineering or closely relevant discipline, or equivalent relevant qualification.
• 15+ years overall professional experience.
• 5+ years Architecture experience.
• 3+ years hands-on Generative AI / Agentic AI development.

6. Ideal Candidate Profile

• The ideal candidate is a hands-on AI architect/technical lead combining deep software architecture capability with recent production GenAI and Agentic AI engineering. They should be able to move from functional requirements to technical architecture, guide an engineering POD, make pragmatic platform choices, and personally contribute to implementation while ensuring enterprise-grade security, governance, observability and AI quality.

Developer – Confluent Kafka (8+ years)

Experience gate: 8+ years total IT experience, with at least 4+ years hands-on on Kafka/Confluent Platform in development roles (not admin or ops only).

Mandatory skills

1. Java (Core + Kafka client APIs). Strong Java with hands-on Producer/Consumer API work. The candidate should understand idempotent producers, transactions, exactly-once vs at-least-once semantics, offset management and consumer groups. Spring Kafka is a plus.
2. Kafka Streams (KStream/KTable). Has built and run stream applications in production, covering stateful operations, joins, windowing, state stores and interactive queries.
3. ksqlDB / KSQL. Has written streams, tables and persistent queries, and promoted them across environments.
4. Kafka Connect, including custom connector development. Has built custom Source/Sink connectors with the Connect API and written Single Message Transforms (SMTs). Has also configured and deployed out-of-the-box connectors. This is the main differentiator from a generic Kafka developer, because the JD asks for custom adaptors and connectors.
5. Performance tuning. Has hands-on tuned producer, consumer and connector configurations (batching, linger, compression, fetch and poll settings) and partition and key strategy. Has diagnosed consumer lag and throughput or latency issues on Confluent clusters.
6. Security implementation at application level. Has configured SASL/SCRAM or OAuth, SSL/mTLS, ACLs and Confluent RBAC in client applications and connectors, plus encryption of data in transit and at rest.
7. Schema Registry and serialization. Has used Avro and/or Protobuf/JSON Schema with Confluent Schema Registry in producers, consumers and connectors.
8. Deployment scripting and environment promotion. Has scripted promotion of topics, connectors, streams and ksqlDB queries to production using shell and/or Python, the Confluent CLI and the Connect/ksqlDB REST APIs. Has worked with at least one CI/CD tool, such as Jenkins, GitLab CI or Azure DevOps.
9. Monitoring and troubleshooting. Has used Confluent Control Center, JMX metrics and logs to debug rebalances, lag, poison-pill messages and dead letter queues (DLQs).
10. Testing and release support. Has written unit tests for Kafka code (TopologyTestDriver, Embedded Kafka or Testcontainers) and has supported SIT, UAT, Staging and Go-Live releases. Comfortable on Linux.

Technical Lead – Confluent Kafka (13+ years)

Experience gate: 13+ years total, with 6+ years hands-on on Kafka/Confluent Platform and at least 2–3 years leading a development team on Kafka/streaming work.

Responsibilities:

• Core Development: Design and develop Confluent Kafka-based applications, custom adaptors, custom connectors, KSQL scripts, and KStream applications.
• Performance Tuning: Execute performance optimization and fine-tuning of custom adaptors and connectors running on Confluent cluster nodes.
• Security Implementation: Implement security architecture models (such as RBAC, ACLs, Encryption, and mTLS/SSL) directly within the applications and connectors.
• Deployment Scripting: Write and prepare Confluent scripts for promoting changes related to Topics, Connectors, Streams, Adaptors, and KSQL into production environments.
• Troubleshooting & Bug Fixing: Perform hands-on monitoring, troubleshooting, and bug fixing to resolve system-related development defects.
• Production Readiness: Prepare and package production deployments, aligning with standard schedules and coordinating with testers for readiness validation.
• Testing & Go-Live: Provide direct, hands-on support during Unit Testing (UT), System Integration Testing (SIT), User Acceptance Testing (UAT), Staging, and Go-Live events.

Mandatory skills:

1. Event streaming requirement analysis and design. Has turned business requirements into stream pipeline designs, covering topic modelling, partitioning and keying strategy, data contracts and event-driven architecture patterns.
2. Audit and retry mechanisms, end to end. Has designed and owned retry topics, DLQs, idempotent processing, replay and reprocessing, and audit trails in production. This is called out explicitly in the JD and is a strong filter.
3. Schema Registry governance. Has set and enforced compatibility modes (BACKWARD, FORWARD, FULL and their transitive variants) and schema evolution rules. Has managed subject naming strategies and built compatibility checks into CI pipelines.
4. Cluster upgrade experience. Has provided development-side guidance on Confluent Platform minor and major upgrades. That includes client and broker compatibility, rolling upgrades, rollback testing in staging, and ideally ZooKeeper-to-KRaft migration exposure.
5. Complex and systemic troubleshooting. Has been the escalation point for issues like rebalance storms, under-replicated partitions or ISR shrink, ordering and duplication problems, and cross-pipeline failures.
6. LLD documentation ownership. Has authored and maintained Low-Level Design documents in Confluence (Atlassian).
7. Technical leadership. Has led code reviews, enforced coding standards, mentored developers, and owned team deliverables through UT, SIT, UAT and Go-Live.