Company: DWT

Business Type: Startup

Company Type: Product &

- Service

Business Model: B2B

Funding Stage: Bootstrapped

Industry: PHARMA

Position Overview

- Openings: 2 Positions
- Industry Context: Pharmaceutical and Life Sciences

Role Summary We are seeking 2 Lead AI Engineers with deep expertise in agentic systems architecture and production LLM deployments. In this role, you will design and ship multi-agent pipelines, advanced RAG architectures, and tool-orchestration layers tailored for life sciences, pharma commercial analytics, and medical affairs workflows. You will serve as a technical pod lead—guiding junior engineers, conducting design reviews, and ensuring enterprise-grade compliance (HIPAA, GxP, 21 CFR Part 11).

Key Responsibilities

- Agentic Architecture: Design and implement multi-agent graphs, tool orchestration layers, memory systems, and retrieval modules using LangGraph, AutoGen, CrewAI, or MCP.
- Production RAG Pipelines: Build and optimize high-performance RAG workflows (document ingestion, chunking, hybrid search, embedding selection, and latency tuning).
- Life Sciences Integration: Integrate agentic systems with pharma platforms (IQVIA, Symphony, Komodo, Veeva Vault, Medidata) via REST, event-driven hooks, and batch patterns.
- Observability &
- Ops: Own component-level telemetry, trace logging, cost tracking, and drift alerts using LangSmith or Helicone.
- Technical Leadership: Lead subsystem designs (agent topology, API contracts,



failure handling), conduct code reviews, and act as the day-to-day technical mentor on the pod.
- Compliance &
- Quality: Apply GxP traceability, HIPAA-compliant handling, and 21 CFR Part 11 auditability patterns across all modules.

Mandatory Requirements &

- Tech Stack

- Experience: 6+ years of software/ML engineering, with 2+ years building and shipping production LLM or agentic AI systems (at least 2 systems taken live to production with benchmarks).
- Programming &
- Cloud: Master-level Python (type annotations, testing, profiling)
- Cloud deployment on AWS, Azure, or GCP using Docker, Kubernetes, Terraform/CDK, and CI/CD.
- Frameworks &
- SDKs: Hands-on proficiency with agentic frameworks (LangGraph, LangChain, AutoGen, CrewAI) and direct SDK usage of Anthropic Claude API, OpenAI Assistants API, or Vertex AI Agent Builder.
- Vector Stores &
- RAG: Expertise with vector databases (Pinecone, Weaviate, pgvector), hybrid search, and evaluation frameworks (RAGAS).
- Domain Context: Exposure to regulated data environments (HIPAA, GxP, 21 CFR Part 11) and pharma commercial data (Rx/claims, medical affairs, or clinical ops).

What We Offer

- Competitive compensation package (CTC inclusive of 10% variable).
- Hybrid flexibility with structured WFH allowances.
- Chance to work at the bleeding edge of agentic AI and Model Context Protocol (MCP) in the life sciences sector.

📌 Artificial Intelligence Engineer (Bengaluru)
🏢 Sourcingxpress
📍 Bengaluru