Senior Technical Lead - Agentic AI
New
J
JobgetherTechnology, AI
Remote flexibility: Fully remote position based in India.Full-TimeSenior
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 and 2+ years building and deploying LLM-based or Agentic AI applications in production.
- Required Skills
- PythonCloud ComputingMLOpsGenerative AI
Requirements
- 10+ years of overall software engineering experience, including 4+ years in AI/ML systems.
- 2+ years building and deploying LLM-based or Agentic AI applications in production.
- Hands-on knowledge of LLM application development, RAG, embeddings, vector databases, prompt engineering, and function calling.
- Experience with Agentic AI frameworks such as LangGraph, AutoGen, or CrewAI.
- Strong Python and software engineering fundamentals for distributed systems.
- Experience with cloud-native architectures (AWS, Azure, or GCP).
- Experience with MLOps/LLMOps tools such as MLflow, LangSmith, or Weights & Biases.
- Knowledge of model fine-tuning and evaluation techniques (LoRA/PEFT, RLHF, offline/online evaluation).
- Demonstrated leadership in owning architecture, providing technical direction, and mentoring engineers.
- Excellent communication skills to translate technical concepts for business stakeholders.
Responsibilities
- Architect, design, and develop Agentic AI and Generative AI solutions from concept and prototyping through production, including multi-step reasoning agents, tool/function-calling workflows, and multi-agent systems.
- Build scalable RAG pipelines covering chunking, embeddings, vector search, hybrid retrieval, prompt engineering, model routing, fine-tuning, and optimization.
- Evaluate and select foundation models based on accuracy, performance, latency, cost, and business requirements.
- Own technical architecture decisions for reliable, scalable, secure, and cost-efficient LLM applications.
- Establish practices for model lifecycle management, deployment, monitoring, evaluation, and hallucination mitigation.
- Lead and mentor AI/ML and backend engineers, conduct architecture and design reviews, and establish engineering standards.
- Partner with Product, Data, Platform, Security, and Compliance teams to ensure AI solutions align with business objectives and responsible-AI principles.
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