Peoplefy
Website:
peoplefy.com
Job details:
Key Responsibilities
Agentic AI Design & Development
- Design, build, and deploy autonomous AI agent systems capable of multi-step reasoning, planning, and task execution across R&D workflows
- Develop multi-agent architectures where specialized agents collaborate, delegate, and coordinate to solve complex pharmaceutical problems
- Implement tool-using agents that interact with APIs, databases, internal systems, and external data sources
- Build feedback loops and self-correction mechanisms to improve agent reliability and accuracy over time
- Leverage AWS AgentCore for agent lifecycle management, memory persistence, and tool integration — deploying and managing agents at scale in a governed, enterprise environment
- Design and implement MCP (Model Context Protocol)-based agent architectures, building MCP servers and clients to standardize how agents interact with external tools, data sources, and internal services
LLM-Based Solution Engineering
- Develop and fine-tune LLM-based applications using frameworks such as LangChain, LlamaIndex, AutoGen, CrewAI, or similar
- Design and implement Retrieval-Augmented Generation (RAG) pipelines to ground agents in proprietary scientific and operational knowledge
- Engineer prompt engineering strategies, chain-of-thought reasoning, and structured output parsing for production-grade reliability
- Evaluate and benchmark LLM performance across models (GPT-4, Claude, Mistral, Llama, etc.) for specific pharmaceutical use cases
- Build and deploy agentic solutions using AWS Bedrock Agents and Bedrock Knowledge Bases, leveraging foundation models available on Bedrock (e.g., Claude, Titan, Llama) for scalable, managed generative AI workloads
Software Engineering & Architecture
- Write clean, maintainable, production-ready Python code following software engineering best practices (SOLID principles, design patterns, code reviews)
- Build RESTful and event-driven APIs to expose agent capabilities to downstream applications and users
- Implement CI/CD pipelines, automated testing (unit, integration, regression), and monitoring for AI systems
- Ensure observability of agent behavior through logging, tracing, and evaluation frameworks (e.g., LangSmith, Arize, Weights & Biases)
Systems Integration
- Integrate agentic solutions with existing digital ecosystem including data platforms, clinical systems, ERP, and knowledge management tools
- Connect agents to vector databases (Pinecone, Weaviate, pgvector, Chroma) for semantic search and memory management
- Work with cloud-native services ( AWS) to deploy scalable, secure, and compliant AI workloads — including AWS Bedrock managed AI services for foundation model access and agent orchestration
- Integrate MCP-compatible tools into agent workflows, enabling standardized, interoperable connections between AI models and external data sources, APIs, and enterprise systems
- Collaborate with data engineers to ensure agents have access to high-quality, governed data pipelines
Collaboration & Delivery
- Partner with product managers, data scientists, and domain experts (biologists, clinicians, regulatory specialists) to translate scientific needs into agentic AI solutions
- Participate in Agile ceremonies (sprint planning, retrospectives, demos) and contribute to team velocity
- Contribute to technical documentation, architecture decision records (ADRs), and internal knowledge sharing
- Mentor junior engineers and contribute to the team's AI engineering standards and best practices
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