Key Responsibilities

- - Lead the design, development, and deployment of AI/ML and Generative AI solutions for business and product use cases.

- Define AI technical strategy, architecture standards, engineering practices, and technology roadmaps across product initiatives.

- Architect and build LLM-powered applications, AI agents, agentic workflows, RAG systems, intelligent automation, and AI-powered decision-support systems.

- Design and implement context engineering approaches to provide AI systems with the right information, tools, memory, instructions, and constraints at the right time.

- Design and build reliable AI harnesses around models, including orchestration, tool use, guardrails, validation, observability, fallback mechanisms, and human-in-the-loop workflows.

- Evaluate and implement models from OpenAI, Anthropic, Google, Meta, Hugging Face, and other AI platforms based on capability, performance, cost, latency, security, and business requirements.

- Develop and optimize prompt and context engineering, embeddings, vector search, fine-tuning, model selection, and inference strategies.

- Design scalable AI architectures using Python, APIs, microservices, distributed systems, and cloud infrastructure.

- Build production-grade AI/ML pipelines covering data preparation, experimentation, evaluation, testing, deployment, monitoring, and optimization.

- Establish LLM and agent evaluation frameworks covering quality, accuracy, hallucination, safety, latency, cost, reliability, and task completion.

- Build automated evaluation and regression-testing mechanisms for AI applications and continuously improve model and system performance using production feedback.





- Lead the integration of AI solutions with existing applications, APIs, databases, enterprise systems, and product workflows.

- Work with engineering teams to deploy AI systems using AWS/Azure/GCP and modern MLOps/LLMOps practices.

- Establish standards for AI security, data privacy, model governance, responsible AI, prompt-injection protection, access control, and AI application observability.

- Design mechanisms for AI system reliability, including validation, guardrails, failure handling, fallback strategies, and controlled model behavior.

- Conduct POCs and technical research on emerging AI technologies and identify opportunities for their application within the business.

- Identify and evaluate opportunities for AI across areas such as financial intelligence, customer experience, personalization, fraud and risk, compliance, financial-document intelligence, trading and investment workflows, and business automation.

- Mentor and guide AI/ML engineers and contribute to technical hiring, capability development, and engineering standards.

- Collaborate with Product Managers, engineering teams, and business stakeholders to convert business requirements into scalable and measurable AI solutions.

- Monitor AI system performance, accuracy, latency, cost,



reliability, and business outcomes and continuously improve production systems.

- Drive AI initiatives from POC to production, ensuring measurable business value and engineering quality.

Required Technical Skills

- - Robust proficiency in Python and AI/ML frameworks.

- Hands-on experience with Generative AI, LLMs, NLP, Machine Learning, and Deep Learning.

- Strong understanding of RAG, embeddings, vector databases, prompt engineering, context engineering, function/tool calling, and AI agents.

- Experience designing agentic workflows, AI orchestration, AI harnesses, guardrails, and human-in-the-loop systems.

- Experience with frameworks such as LangChain, LlamaIndex, PyTorch, TensorFlow, or equivalent.

- Experience working with OpenAI, Azure OpenAI, Anthropic, Gemini, Hugging Face, or similar LLM platforms.

- Strong knowledge of REST APIs, microservices, databases, distributed systems, and cloud architectures.

- Experience with vector databases such as Pinecone, FAISS, Weaviate, Milvus, Chroma, or equivalent technologies.

- Hands-on experience with AWS/GCP/Azure AI and cloud services.

- Knowledge of Docker, Kubernetes, CI/CD, Git, and MLOps/LLMOps.

- Strong understanding of AI/LLM evaluation, benchmarking, monitoring, observability, optimization, and production deployment.

- Knowledge of AI/LLM security, including data protection, prompt injection, jailbreak risks, model risks, access controls, and secure tool execution.

- Understanding of AI system reliability, cost optimization, latency optimization, and scalable inference architectures.

📌 Lead AI Engineer (Nagar)
🏢 FINVASIA CAREER
📍 Nagar