AI Platform Lead Engineer-IN
The AI Platform Lead Engineer builds and maintains the systems that support AI and machine learning applications, working to ensure the systems are scalable, efficient, and reliable.
Key Responsibilities and Duties Deploy, manage and scale AI systems, as well as support model activity and deployment infrastructure in cloud environments
Work with infrastructure and application development teams, data engineers, and model owners, to better integrate the AI applications into mainstream enterprise applications.
Put into production the ML models keeping in mind the scalability, optimization, resource availability, and security for the AI solution.
Educational Requirements University (Degree) Preferred
Work Experience 5+ Years Required; 7+ Years Preferred
Physical Requirements Physical Requirements: Sedentary Work
Career Level
8IC
Total experience required : Between 9 to 15 Years
Job location : Mumbai / Pune
Responsibilities
Design and implement end-to-end Generative AI solutions that address complex business challenges, with a strong focus on scalability, reliability, and production readiness.
Build and maintain Retrieval-Augmented Generation (RAG) pipelines by integrating vector databases such as Pinecone, Weaviate, and FAISS with embedding models and large language models (LLMs) to deliver context-aware, knowledge-grounded responses at scale.
Develop and optimize prompting strategies, templates, and workflows that maximize LLM accuracy, consistency, and performance across diverse use cases and user scenarios.
Customize and fine-tune foundation models using techniques such as supervised fine-tuning (SFT), reinforcement learning from human feedback (RLHF), and parameter-efficient methods including LoRA and QLoRA to meet domain-specific performance requirements.
Establish and maintain rigorous model evaluation frameworks that measure accuracy, latency, cost-efficiency, hallucination rates, and task-specific metrics, conducting A/B testing and comparative analysis to drive informed model selection and configuration decisions.
Implement comprehensive observability and monitoring systems — including logging, tracing, and alerting — to continuously track model behavior, prompt-response patterns, token consumption, error rates, and performance drift in production environments.
Design and deploy production-grade AI agents leveraging both low-code platforms and custom high-code implementations, balancing flexibility, maintainability, and performance to meet evolving business needs.
Integrate a broad range of AI and ML capabilities — including natural language processing, computer vision, document intelligence, and traditional machine learning models — to build holistic, multi-modal solutions that solve complex problems.
Continuously evaluate the rapidly evolving AI landscape, proactively researching and adopting emerging models, frameworks, and tools to enhance solution quality and sustain competitive advantage.
Collaborate cross-functionally with product, engineering, and business stakeholders to translate requirements into scalable AI architectures, ensuring alignment between technical solutions and organizational objectives.
Contribute to the development of best practices, reusable components, and internal standards for Generative AI development, fostering a culture of innovation, quality, and responsible AI use across the organization.
Must-Have Skills
Proven hands-on experience building and deploying Generative AI solutions, including RAG pipelines, prompt engineering, and LLM fine-tuning using frameworks such as LangChain, LlamaIndex, or simila
Strong proficiency in Python and experience working with LLM APIs (OpenAI, Anthropic, Hugging Face) and vector databases such as Pinecone, Weaviate, or FAISS
Solid understanding of model evaluation methodologies, including benchmarking, A/B testing, hallucination detection, and performance monitoring in production environments
Experience with fine-tuning techniques…