Senior Technical Lead - Generative AI
New
J
JobgetherSecurity & IT
IndiaFull-TimeLead
Salary3,000,000 - 5,000,000 INR per year
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Job Details
- Experience
- 10+ years of overall software engineering experience, including at least 4 years working directly with AI/ML systems.
- Required Skills
- PythonCloud ComputingMicroservicesLLMMLOpsGenerative AI
Requirements
- 10+ years of overall software engineering experience, including at least 4 years working directly with AI/ML systems.
- 2+ years of hands-on experience building and deploying LLM-based or Agentic AI applications in production environments.
- Deep expertise in LLM application development, RAG, embeddings, vector databases, prompt engineering, and AI-agent architectures.
- Practical experience with multi-agent systems, tool/function calling, memory management, and reasoning workflows.
- Strong Python and software engineering fundamentals with experience building scalable, distributed, production-grade systems.
- Experience designing APIs, microservices, and cloud-native architectures on AWS, Azure, or GCP.
- Hands-on experience with MLOps or LLMOps platforms such as MLflow, LangSmith, or Weights & Biases.
- Working knowledge of LLM fine-tuning and evaluation techniques, including LoRA/PEFT and RLHF.
- Proven technical leadership experience, including architecture ownership, mentoring, and technical reviews.
- Excellent communication and stakeholder-management skills.
Responsibilities
- Architect, design, and develop Agentic AI and Generative AI solutions from early concepts and prototypes through production deployment.
- Build multi-step reasoning agents, tool and function-calling workflows, memory systems, planning capabilities, and multi-agent architectures.
- Design and productionize scalable Retrieval-Augmented Generation (RAG) pipelines covering chunking, embeddings, and vector search.
- Establish engineering standards for AI testing, observability, guardrails, hallucination mitigation, and production reliability.
- Drive AI/LLMOps practices covering model lifecycle management, deployment, and continuous improvement.
- Lead, mentor, and develop AI/ML and backend engineering teams while maintaining strong technical standards.
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