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.
Pay: ₹135,000.00 - ₹140,000.00 per month
Work Location: In person
