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