Overview

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.

Responsibilities

• 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.
• 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.

Qualifications

• 5+ Years Required; 7+ Years Preferred
• Physical Requirements: Sedentary Work
• 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
• Demonstrated ability to design and deploy production-grade AI systems with robust observability, logging, and monitoring capabilities ensuring reliability and performance at scale
• Exposure to multi-modal AI capabilities including computer vision, document intelligence, or speech processing integrated within broader AI/ML pipelines
• Familiarity with low-code AI agent platforms (such as Microsoft Copilot Studio, Flowise, or similar) alongside the ability to build custom high-code agent implementations
• Knowledge of responsible AI principles, including bias detection, fairness evaluation, and compliance considerations relevant to regulated industries such as financial services
• Familiarity with MLOps practices and tools such as MLflow, Weights & Biases, or similar platforms for experiment tracking, model versioning, and deployment automation

Education

• University (Degree) Preferred