position overview
the senior ai engineer reports to the head of technology and is responsible for the entire lifecycle of ai solutions, from design to deployment. this includes using multi-agent workflows, advanced knowledge retrieval, and large language models to solve enterprise challenges, thereby improving business efficiency, innovation, and strategic decision-making. the role involves leading projects such as ai copilots, enterprise search solutions, summarization engines, and intelligent automation tools, ensuring they are robust, scalable, and production-ready. a key aspect of the role is mentoring junior ai engineers and maintaining high engineering standards. this is a full-time hybrid position based in mumbai.
key responsibilities
core functional responsibilities
● build and deploy agentic systems and multi-agent workflows using frameworks like langchain, llamaindex, and openai sdk.
● develop rag pipelines using embeddings and vector databases (., faiss, pinecone, weaviate, qdrant).
● fine-tune and optimize llms (gpt-4, claude, llama, mistral, gemini) using techniques like lora/qlora and quantization.
● ensure solutions are secure and compliant, with guardrails, safe api usage, and pii protection.
● implement agent monitoring and evaluation with tools like langsmith or agentops to track and improve performance.
● deliver real-world enterprise use cases such as ai copilots, workflow orchestration, and intelligent search systems.
● mentor junior ai engineers, reviewing their work, sharing best practices, and supporting their growth.
● collaborate with product, data, and devops teams to bring ai solutions to production at scale. demonstrated knowledge of agentic ai frameworks - practical experience in small language model (slm) implementation.
required skills & experience
general & cross-functional
● strong knowledge of llms and gen-ai applications, with hands-on experience in fine-tuning and prompt engineering.
● proven experience in rag pipelines and working with vector databases.
● practical expertise in agent frameworks (langchain, llamaindex, etc.) and multi-agent orchestration.
● ability to deploy and scale models on cloud platforms (aws, azure, huggingface, etc.).
● proficiency in python or similar programming languages.
● experience with llmops (monitoring, evaluation, cost optimization).
● strong track record of delivering enterprise-grade ai solutions, not just prototypes.
● familiarity with agent monitoring/debugging tools (langsmith, agentops).
● experience mentoring or guiding junior engineers in a technical team.
● (bonus) experience fine-tuning open-source llms or contributing to community projects.
● (bonus) exposure to autonomous agent use cases such as research copilots or intelligent task orchestration
6 - 8 Years
1 Openings
Not Disclosed
Online interview
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