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Global AI/ML Engineer Manager
Boston Consulting Group
Gurugram, IND
Full-time
Senior · 8+ years experience
Onsite
Discovered 3 weeks ago
Claude CodeCursorPythonFastAPIOpenAIAnthropic
Free
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Key Skills for This Role
Claude CodeCursorPythonFastAPIOpenAIAnthropic
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Must-Have Skills
- Hands-on, daily use of AI-assisted and agentic coding tools (e.g., Claude Code, Cursor, GitHub Copilot, autonomous coding agents) to write and refactor code, automate workflows, and optimize engineering processes.
- Strong experience with Python, particularly in building REST APIs using frameworks like FastAPI.
- Grounding in NLP and machine learning as they relate to building LLM systems
- Strong experience working with key LLM models APIs (e.g. OpenAI, Anthropic)
- Experience building, deploying, and securing MCP servers at scale.
- Understanding of multi-agent systems and their applications in complex problem-solving scenarios.
- Designing and implementing RAG systems end to end: vector databases, semantic search, retrieval quality, and chunking strategy.
- Experience with distributed search and indexing technologies (e.g., Elasticsearch, OpenSearch, Solr)
- Experience with prompt writing for various use cases
- Experience with Java or confidence in agentic coding skills to develop in Java
- Experience with generative solutions released to prod, at scale, beyond POCs
- Proficiency with server-side events, event-driven architectures, and messaging systems.
- Strong critical thinking and systems thinking skills, with experience debugging, optimizing, and making sound engineering decisions across complex backend systems, not just solving isolated problems.
- Solid understanding of security best practices for backend systems, including authentication and data protection.
Nice-to-Have Skills
- Experience with LLM guardrails
- Experience with LLM Frameworks (e.g. LangChain, LlamaIndex)
- Experience with LLM monitoring and observability
- Experience developing AI/ML technologies within large and business critical applications
- Building evaluation into LLM systems: eval harnesses, regression suites, LLM-as-judge, and offline/online quality metrics.
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