Design and implement context architectures for LLM apps and agents: schemas, memory patterns, context assembly, and context window management.
Build and optimize RAG pipelines (chunking, embeddings, hybrid retrieval, re-ranking) and validate quality using repeatable evaluation harnesses.
Develop system prompts, prompt libraries, and structured output patterns; harden solutions against prompt injection and jailbreak attempts.
Implement agentic workflows and tool-use integrations (APIs, function calling, workflow engines) with clear guardrails and observability.
Engineer memory and persistence patterns (session memory, episodic recall, vector memory) appropriate for enterprise privacy and retention needs.
Work with enterprise data and app teams to connect AI solutions to real systems (ERP/SCM/CRM, data lakes/warehouses), ensuring secure access and correct semantics.
Collaborate with delivery leads to break work into stories, estimate effort, and drive day-to-day execution; mentor engineers through reviews and pairing.
Use systematic optimization (prompt/context tuning, retrieval experiments, DSPy-style approaches) to improve reliability, latency, and cost.
What You Bring
AI Engineering Skills
Strong Python skills and ability to ship production services.
Hands-on expertise with RAG: embeddings, vector stores, retrieval strategies, re-ranking, and grounding techniques.
Strong prompt and context engineering: system prompts, structured outputs, tool-use prompting, and context assembly patterns.
Experience building agentic systems with orchestration frameworks (or custom implementations) and designing safe tool integrations.
Awareness of security threats (prompt injection, data exfiltration) and ability to implement practical mitigations and guardrails.
Enterprise Integration Background
Experience integrating AI apps with enterprise services, data sources, and identity (SSO/IAM), including secure network and secrets handling.
Ability to work with structured and unstructured enterprise data; understand governance/lineage enough to avoid incorrect or unsafe data use.
Comfort operating within enterprise SDLC controls: CI/CD, change management, security reviews, and production incident response.
Working knowledge of enterprise workflows and process context so AI solutions map to real operations and decision points.
Tools & Platforms
Python; common LLM/RAG frameworks (LangChain, LlamaIndex, Haystack or equivalent).
Vector databases and search stacks (pgvector, Pinecone, Weaviate, Milvus, Elasticsearch/OpenSearch).
Memory and state management approaches (session stores, vector memory, durable stores) appropriate for privacy and retention constraints.
Evaluation and observability tooling (RAGAS, LangSmith/Phoenix or equivalent) and ability to build custom eval pipelines.
Preferred Qualifications
B.Tech / M.Tech in CS, Engineering, or Linguistics; research background in NLP or information retrieval is a plus.
Published work, open-source contributions, or internal frameworks related to context management or prompt engineering.
Prior consulting or professional services experience — ability to adapt context design to diverse client environments quickly
