Gen AI Developer
- Tata Consultancy
- 4 - 8 years
- Hyderabad
- 22 hours ago
- Email to a friend
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Job Description
Job Description:
Must-Have**
(Ideally should not be
more than 3-5)
LLM Ops & Agent Orchestration: Experience with prompt engineering, prompt/version management, model routing, and evaluation using agentic and orchestration frameworks such as LangChain, LangGraph, Google Agent Development Kit (ADK), and other agent-based LLM frameworks.
RAG Expertise: Practical experience building and tuning RAG pipelines (chunking strategies, embeddings, retrievers, reranking).
Vector DBs: Hands-on with at least one: FAISS, Pinecone, Milvus, Weaviate (index types, parameters, scaling).
Python Engineering: Strong Python with FastAPI/Flask, async IO, typing, packaging; clean code & SOLID principles.
MLOps/Platform: Docker, Kubernetes, Git, CI/CD (Jenkins/GitHub Actions/Azure DevOps), observability (logs/metrics/traces).
APIs: Design/consume REST APIs, OpenAPI/Swagger, pagination, error models, rate limiting.
Data Handling: Experience with text preprocessing, embeddings, metadata schemas, and storage (object stores/RDBMS).
Build production-grade RAG pipelines, manage vector databases, and own LLM Opsincluding observability, evaluation, and cost/performance optimizationon secure, compliant platforms.
Good-to-Have
Agentic Architectures: Practical experience comparing and implementing LangChain/LangGraph vs Google ADK and other agentic solutions, understanding trade-offs across control flow,
memory,
observability, tool-calling, scalability, and production readiness.
Retrieval & Ranking: BM25, hybrid search, approximate nearest neighbor configs, rerankers (e.g., cross-encoders).
Model Ecosystem: Experience with OpenAI, Azure OpenAI, Anthropic, Cohere, local models (Llama, Mistral) and serving frameworks (vLLM/Triton).
Guardrails & Safety: Tools like Guardrails.ai, LlamaGuard, content moderation APIs; jailbreak detection.
Evaluation & Tracing: LLM traces (LangSmith, Phoenix), synthetic eval sets, human-in-the-loop feedback.
Infra as Code: Terraform/Helm; secrets management (Vault/KMS).
Streaming & Queues: Kafka/RabbitMQ; event-driven RAG updates.
Search Platforms: Elastic/OpenSearch integrations; hybrid retrieval pipelines. Caching & Cost Controls: Redis, response caching, token usage optimization.
Job Classification
Industry: IT Services & ConsultingFunctional Area / Department: Data Science & AnalyticsRole Category: Data Science & Machine LearningRole: Machine Learning EngineerEmployement Type: Full timeContact Details:
Company: Tata ConsultancyLocation(s): Hyderabad
Keyskills: Generative Ai MLOps Vector Db Retrieval Augmented Generation Python Data Handling LLM Ops API
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