Walk-in || Direct Walk-in @ Bangalore / AI ML Engineer

Job Title: AI / ML Engineer Location: Bangalore, India Employment Type: Full-time Relevant Experience: 5+ Years in software engineering, with 2+ years building production AI/ML or LLM systems About PennEngineering PennEngineering offers innovative fastening solutions for a variety of applications across industries like Automotive Electronics, Consumer Electronics, Datacom and more. Job Summary As PennEngineering accelerates its Speed of Now transformation, the Customer Experience (CX) team is responsible for customer-facing digital products. We are looking for an AI / ML Engineer to help design and build the AI foundation and agentic capabilities that let customers find the right fasteners and installation machines, and receive a quote, through natural, conversational experiences. Behind those experiences sits a real machine learning and data challenge: ingesting and structuring product data from our PIM, interpreting the engineering drawings and product images in our catalogues, and matching all of it against a customer's stated requirement to recommend the correct part. This is a hands-on role that combines applied ML, multimodal and retrieval techniques, modern LLM and agent engineering, and disciplined production deployment on AWS. You will partner closely with the Software Architect, the Senior Data Architect, and the engineers building the CX product portfolio. Key Responsibilities Product Data & Multimodal Understanding Build pipelines that ingest, clean, and structure product and attribute data from the PIM and other enterprise sources into a form that recommendation and retrieval models can use Extract meaning from product catalogues that are not purely textual, including engineering drawings, dimensioned diagrams, and product images, using vision and document-understanding models Design and maintain the embedding strategy for products, attributes, and customer requirements, including the choice of text and multimodal embedding models and how they are versioned and refreshed Build and operate the vector stores and retrieval indexes that make product knowledge searchable by meaning, not just keywords Agentic Find, Quote & Recommendation Build the agentic experiences that let a customer describe a need in their own words and be guided to the right fastener or installation machine, including tool and function calling, retrieval, multi-step planning, and guardrails Develop the recommendation and ranking logic that matches a customer requirement against the product catalogue, combining semantic similarity, structured-attribute filtering, and compatibility rules Build the agentic quoting flow on top of the recommendation layer so that a validated selection can move to a quote with minimal friction Implement retrieval-augmented generation patterns that ground agent responses in accurate, current PennEngineering product knowledge AI Factory, Production & Integration Develop and operationalise the AI Factory: reusable patterns for the feature store, model registry, prompt management, fine-tuning, evaluation harness, and monitoring Implement responsible AI controls across all of the above: evaluation, red-teaming, content safety, observability, and cost guardrails Partner with the Software Architect to integrate AI capabilities cleanly into the front end and microservices backend, and with the Senior Data Architect on the features, datasets, and embeddings these capabilities depend on Ship ML and LLM features to production with proper testing and monitoring, and stay current with AWS's AI roadmap (Bedrock, SageMaker, AgentCore-style services) to recommend what to adopt and when What Does Success Look Like Success is AI capabilities that customers and internal teams trust enough to rely on. Agentic Find and Agentic Quoting work accurately, fail gracefully, and improve over time because the evaluation and monitoring you built make their performance visible. The AI Factory means new AI features are built on reusable patterns rather than from scratch each time. You hold a high bar for responsible AI, and the safety, observability, and cost controls you put in place are the reason leadership is comfortable putting AI in front of customers. Required Skills & Qualifications Five or more years of software engineering experience, including two or more years building production AI/ML or LLM-based systems Strong, hands-on understanding of embeddings and embedding models, including how to choose, evaluate, and apply text and multimodal embeddings for semantic search and recommendation Experience with vector databases and modern retrieval patterns, including retrieval-augmented generation Experience building recommenda Job Title: AI / ML Engineer Location: Bangalore, India Employment Type: Full-time Relevant Experience: 5+ Years in software engineering, with 2+ years building production AI/ML or LLM systems About PennEngineering PennEngineering offer