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

Key Requirements:

Experience: 6–8+ years of relevant AI/ML experience

• Strong technical background in AI/ML, Generative AI, and enterprise AI implementations.
• Mandatory experience with Google Gemini Enterprise and Microsoft Copilot ecosystem.
• Ability to validate business use cases and assess their technical feasibility and business value.
• Drive solution design and ensure alignment with customer objectives and best practices.
• Oversee overall AI initiative execution, ensuring optimal utilization of resources and timely delivery.
• Provide technical governance, architecture guidance, and quality assurance across AI projects.
• Act as the primary technical point of contact for customer stakeholders.
• Mentor junior team members and support adoption of AI solutions across business functions.

Key Roles & Responsibilities

• Understand customer requirements and design scalable cloud solutions.
• Conduct discovery workshops, dependency mapping and migration assessments.
• Define migration strategies, landing zones and solution roadmaps.
• Prepare HLD/LLD, architecture diagrams, RFP/RFI responses, SOWs and commercial proposals.
• Deliver customer presentations, solution demos, workshops and PoCs.
• Plan and execute migration engagements using native and partner migration tools.
• Collaborate with Sales, Delivery and Partner teams.

Primary Skills

Solution Architecture, Migration Strategy, RFP/RFI, Proposal Writing, Customer Presentations, HLD/LLD, Multi-Cloud Platforms



Key Roles & Responsibilities

• Build and manage CI/CD pipelines.
• Automate infrastructure using Terraform, CloudFormation and Bicep.
• Deploy and manage containerized workloads.
• Configure monitoring and observability.
• Implement secrets management.
• Implement DevSecOps controls.
• Automate deployments and rollback.
• Collaborate with development teams.

Primary Skills



• Terraform, CI/CD, GitHub Actions, Azure DevOps, Docker, Kubernetes, Git, Monitoring, Secrets Management

• 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.