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 Responsibilities:

• Participate in requirements analysis
• Write clean, scalable code using .NET programming languages
• Test and deploy applications and systems on azure cloud
• Refactor and debug code.
• Develop documentation throughout the software development life cycle (SDLC).

Required Knowledge & Skills:

• 6 - 10 years’ experience in various software development/maintenance roles and proven experience working on .Net project under an Agile /Scrum methodology and extensively used.
•  Experience with Real Estate IT systems would be advantage.

Required Skill:

• .Net Core & Asp.NET 4.0
• C#/VB.NET
• Web API
• Dependency Injection and Web Service Technologies.
• JQuery
• HTML5
• Version control systems like TFS, GIT.
• Good to have knowledge of AWS
• Experience in Angular & Angular JS is must
• Experience in doing peer reviews and code reviews.
• ORM tools like Entity Framework, Dapper
• Unit Testing Framework like NUnit and XUnit
• Exposure to Source control like TFS, GIT
• Understanding of Agile methodologies
• Understanding of Enterprise Frameworks, Release procedures, understanding of development environments.

Key Responsibilities

• RAG Pipelines: Design and implement end-to-end Retrieval-Augmented Generation systems — including chunking strategies, embedding models, vector stores, hybrid search, and re-ranking — to deliver accurate, context-grounded LLM responses.
• Agentic AI Development: Build autonomous and multi-agent AI workflows using frameworks such as LangChain, LangGraph, AutoGen, CrewAI, or Semantic Kernel; implement tool-use, planning, memory, and orchestration patterns.
• Knowledge Graphs: Model, build, and query knowledge graphs using Neo4j and other Graph Databases; integrate graph-based retrieval (GraphRAG) with LLM pipelines for enhanced reasoning and explainability.
• LLM Integration: Integrate and fine-tune Large Language Models (LLMs) using prompt engineering, function calling, structured outputs, and parameter-efficient techniques (LoRA/QLoRA) where applicable.
• Deployment & MLOps: Containerize and deploy GenAI services on AWS, Azure, or GCP; implement monitoring, evaluation, versioning, and cost-efficient scaling for AI workloads.
• Responsible AI: Apply guardrails to mitigate hallucinations, prompt injection, bias, and data leakage; contribute to evaluation frameworks for model accuracy and safety.
• Collaboration: Partner with cross-functional teams, document technical designs clearly, and communicate trade-offs effectively with both technical and non-technical stakeholders.

Required Technical Skills

• Generative AI: Strong hands-on experience building GenAI applications using LLMs (OpenAI GPT, Anthropic Claude, Llama, Mistral, Gemini, etc.); solid grasp of Transformer architectures, embeddings, and prompt engineering.
• RAG: Proven experience designing RAG pipelines — chunking, embeddings, vector databases (Pinecone, Chroma, Weaviate, Milvus, FAISS, pgvector), hybrid search, and re-ranking.
• Agentic AI & Tools: Hands-on experience with Agentic AI frameworks and tools such as LangChain, LangGraph, AutoGen, CrewAI, Semantic Kernel, LlamaIndex, or similar; familiarity with MCP and function/tool calling patterns.
• Neo4j & Graph Databases: Practical experience with Neo4j (Cypher query language), graph data modeling, and integrating Graph DBs into AI/LLM workflows (GraphRAG is a strong plus).
• Programming: Strong Python skills; experience with frameworks such as PyTorch, TensorFlow, FastAPI, or similar; familiarity with REST APIs and async patterns.
• Cloud & Infrastructure: Working knowledge of at least one major cloud platform — AWS (Bedrock, SageMaker), Azure (Azure OpenAI, AI Foundry), or GCP (Vertex AI); comfortable with Docker, Git, and CI/CD pipelines.
• Data Handling: Comfort working with structured and unstructured data, ETL processes, and SQL/NoSQL databases.

Experience & Qualifications

• Experience: Preferably 5–6 years of overall software/AI engineering experience, with meaningful hands-on exposure to Generative AI projects.
• Education: Bachelor’s or Master’s degree in Computer Science, Data Science, Artificial Intelligence, or a related field.
• Communication: Good written and verbal communication skills; able to explain complex AI concepts clearly to both technical and non-technical audiences.
• Problem-Solving: Strong analytical and debugging skills with a product-oriented mindset and a passion for delivering measurable business outcomes.
• Ownership: Self-driven, collaborative, and able to own features end-to-end from design through deployment.

• Architect, design, and implement highly available, scalable, and fault-tolerant cloud infrastructure on Amazon Web Services (AWS) and Microsoft Azure.
• Design multi-account strategies, landing zones, RBAC models, governance frameworks, and cost optimization (FinOps) practices.
• Lead cloud migration initiatives from on‑premises to cloud, including re-hosting, re-platforming, and re‑architecting.
• Design and implement disaster recovery (DR) and multi-region high availability architectures.
• Build, standardize, and optimize enterprise-scale CI/CD pipelines using Jenkins and Azure DevOps.
• Implement Git branching strategies, pull request workflows, tagging strategies, and release governance.
• Integrate DevSecOps practices including automated code scanning, container image scanning, and policy enforcement within pipelines.
• Design and manage production‑grade Kubernetes clusters with autoscaling, RBAC, network policies, and workload optimization.
• Manage container lifecycle using Docker, including private registry management and image security best practices.
• Administer and manage artifact repositories using JFrog Artifactory.
• Manage and govern Kubernetes clusters through enterprise platforms such as Rafay Systems.
• Develop reusable Infrastructure as Code (IaC) modules using Terraform and leverage the Terraform Registry effectively.
• Implement remote state management, locking mechanisms, and infrastructure policy controls.
• Integrate secrets management solutions including HashiCorp Vault, Akeyless Vault, and Azure Key Vault with cloud and Kubernetes workloads.
• Implement secure key rotation, dynamic secrets, and certificate lifecycle management.
• Design and manage cloud networking including VPC/VNet architecture, route tables, subnets, NAT gateways, security groups, NSGs, and load balancers.
• Troubleshoot complex networking issues across AWS and Azure environments.
• Implement monitoring, alerting, and observability using Datadog, including dashboards, SLOs, and distributed tracing.
• Lead production incident management, perform root cause analysis (RCA), and implement preventive measures.
• Ensure high availability and zero-downtime deployments for internet‑facing production applications.
• Administer and troubleshoot Linux systems including Ubuntu and CentOS, with performance tuning and patch management.
• Support and enable data governance and data quality platforms such as Soda and Collibra at the infrastructure level.
• Drive automation initiatives to eliminate manual operational tasks and improve deployment efficiency.
• Implement infrastructure security best practices aligned with enterprise compliance requirements.
• Mentor junior engineers, conduct architecture reviews, and drive DevOps best practices across teams.
• Provide hands-on troubleshooting support for critical, internet‑facing production systems with strict uptime SLAs.

• Seeking a Full Stack Developer with strong Angular (18, 19, 20) expertise and solid Golang backend experience to build and modernize enterprise-scale web applications. The role focuses on high-performance UI development and scalable backend services in an Agile environment.

Key Skills

• Front End (Primary): Angular 18, 19 20, TypeScript, RxJS, HTML5, CSS3, Responsive UI
• Back End (Primary): Golang, RESTful APIs, Microservices
• Authentication, authorization, secure APIs
• Enterprise application and legacy modernization experience

Experience Required

• 5+ years of hands-on Angular development
• 5+ years of backend development using Golang
• Strong full-stack integration experience

Responsibilities

• Build and modernize Angular-based front-end applications
• Design and develop Golang REST APIs and backend services
• Ensure scalability, performance, and reliability
• Collaborate with product, UX, QA, and DevOps teams
• Mentor junior developers and participate in code reviews

Nice to Have

• Micro frontends, cloud platforms (AWS/Azure), CI/CD
• Docker/Kubernetes exposure
• Agile/Scrum experience

Key Requirements:

• 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.