Sr. Specialist, AI Engineering

Merck Merck · Pharma · Telangana, India

The role focuses on the end-to-end development of GenAI applications, including prompt engineering, fine-tuning, RAG pipelines, and LLM-based workflows. Key responsibilities include designing and implementing Agentic AI systems, developing multi-agent systems with planning and tool usage, applying advanced prompt engineering techniques, establishing AgentOps practices for production AI systems, and building multimodal AI solutions. The role also involves harness engineering, developing agent tooling, collaborating with stakeholders, leveraging AI developer tools, and supporting deployment on cloud platforms.

What you'd actually do

  1. Design and implement Agentic AI systems leveraging frameworks such as LangChain, Google ADK, Strands, and Semantic Kernel, enabling multi-step reasoning and task orchestration.
  2. Develop and operationalize multi-agent systems, incorporating patterns such as planning, tool usage, memory, and iterative reasoning.
  3. Apply advanced prompt engineering techniques (ReAct, few-shot learning, chain-of-thought, prompt chaining) to improve accuracy, determinism, and robustness of LLM systems.
  4. Establish AgentOps practices, including observability, tracing, logging, and performance monitoring for production-grade AI systems.
  5. Build multimodal AI solutions, integrating text with images or structured data to support complex enterprise use cases.

Skills

Required

  • RAG architectures
  • prompt engineering techniques
  • LLM-powered applications and agent-based systems in production environments
  • AgentOps practices
  • Python programming skills
  • tooling, function-calling, and agent-driven integrations
  • context engineering strategies
  • harness engineering approaches
  • Model Context Protocol (MCP)
  • Agent-to-Agent (A2A) interaction patterns
  • ML/AI pipelines
  • NLP and text processing
  • transformer architectures and modern LLM ecosystems
  • basic machine learning

Nice to have

  • advanced agentic patterns
  • multimodal AI systems
  • LLM evaluation techniques
  • advanced chunking strategies
  • LLMOps/MLOps practices

What the JD emphasized

  • Agentic AI systems
  • multi-agent systems
  • prompt engineering techniques
  • AgentOps practices
  • harness engineering practices
  • agent tooling and integrations
  • Model Context Protocol (MCP)
  • Agent-to-Agent (A2A)
  • RAG architectures
  • LLM-powered applications and agent-based systems in production environments
  • AgentOps practices
  • tooling, function-calling, and agent-driven integrations
  • context engineering strategies
  • harness engineering approaches
  • Model Context Protocol (MCP)
  • Agent-to-Agent (A2A)

Other signals

  • design and implement Agentic AI systems
  • Develop and operationalize multi-agent systems
  • Apply advanced prompt engineering techniques
  • Establish AgentOps practices
  • Build multimodal AI solutions