Job Title: Senior AI/ML Developer - Generative AI & LLM

Job Summary

We are looking for an experienced Senior AI/ML Developer with strong hands-on expertise in Python, Machine Learning, Deep Learning, Natural Language Processing (NLP), Generative AI, and Large Language Models (LLMs).

The ideal candidate will be responsible for designing, developing, training, evaluating, optimizing, and deploying enterprise-grade AI/ML solutions. The role requires practical experience building LLM-powered applications, Retrieval-Augmented Generation (RAG) pipelines, semantic search solutions, and AI-driven applications integrated with enterprise systems.

The candidate will collaborate with data engineers, solution architects, application developers, cloud and DevOps teams, QA, and business stakeholders to translate business requirements into scalable, secure, and production-ready AI solutions.

The role also involves technical leadership, delivery squad guidance, and direct collaboration with US-based customers.

Key Responsibilities

1. AI/ML Model Development

• Analyze business and technical requirements and design scalable AI/ML solutions aligned with enterprise architecture and business objectives.

• Develop, train, evaluate, optimize, and deploy machine learning and deep learning models using Python.

• Perform data preparation, feature engineering, model experimentation, training, validation, and performance optimization.

• Apply machine learning and deep learning algorithms to solve complex business problems.

• Work with frameworks such as TensorFlow and PyTorch.

• Document model architectures, experiments, evaluation results, and implementation approaches.

2. Generative AI & LLM Development

• Design and develop Generative AI applications using Large Language Models (LLMs).

• Implement prompt engineering, model orchestration, response evaluation, and guardrails.

• Build enterprise-grade LLM applications integrated with business systems and applications.

• Apply NLP techniques, embeddings, semantic search, and vector-based retrieval.

• Design and implement Retrieval-Augmented Generation (RAG) architectures to generate accurate and contextually grounded responses.

• Integrate vector databases and orchestration frameworks into AI-powered applications.

• Evaluate LLM performance, response quality, relevance, accuracy, and reliability.

• Explore Agentic AI concepts and AI assistants to automate complex business workflows.

3. Enterprise Integration & Production Deployment

• Integrate AI/ML models into enterprise applications through REST APIs, microservices, and application integration frameworks.

• Collaborate with application developers, data engineers, cloud architects, and DevOps teams to implement scalable AI solutions.

• Implement production-readiness practices, including automated testing, CI/CD, containerization, and model versioning.

• Establish model monitoring, observability, drift detection, and performance optimization practices.

• Troubleshoot production issues and continuously improve model reliability and scalability.

• Ensure solutions comply with enterprise security, privacy, governance, and responsible AI standards.

4. Collaboration & Technical Leadership

• Work closely with architects, data engineers, application teams, QA, product teams, and business stakeholders.

• Translate business requirements into technical designs and scalable AI/ML solutions.

• Guide development squads and provide technical direction throughout the implementation lifecycle.

• Collaborate directly with US-based customers to understand requirements, present solutions, and communicate technical recommendations.

• Support knowledge sharing, technical mentoring, code reviews, and engineering best practices.

Required Technical Skills & Qualifications

• Strong hands-on expertise in Python programming.

• End-to-end experience in machine learning model development, including data preparation, feature engineering, training, evaluation, tuning, and deployment.

• Strong knowledge of machine learning and deep learning algorithms and model architectures.

• Hands-on experience with TensorFlow and/or PyTorch.

• Strong experience developing Generative AI and LLM-based applications.

• Practical knowledge of prompt engineering, LLM orchestration, model evaluation, and guardrails.

• Strong understanding of Natural Language Processing (NLP).

• Experience with embeddings, semantic search, and vector databases.

• Hands-on experience designing and implementing Retrieval-Augmented Generation (RAG) pipelines.

• Experience integrating AI/ML models with enterprise applications using APIs and microservices.

• Understanding of CI/CD, automated testing, containerization, model versioning, and production deployment.

• Knowledge of model monitoring, observability, drift detection, and performance optimization.

• Ability to translate business requirements into scalable technical solutions.

• Strong communication, problem-solving, collaboration, and stakeholder management skills.

Preferred Technical Skills

• JavaScript, Node.js, and TypeScript for full-stack development and service integration.

• Experience with Agentic AI concepts, AI assistants, and autonomous workflows.

• Experience with GitHub Actions for build, testing, security scanning, and deployment automation.

• Guidewire platform knowledge or experience working with Property and Casualty (P&C) insurance applications.

• Experience in BFSI, insurance, or other high-volume enterprise application environments.

Leadership & Client-Facing Skills

• Experience leading or guiding technical delivery squads.

• Ability to provide technical direction and mentor development team members.

• Strong stakeholder management and collaboration skills.

• Excellent verbal and written communication skills.

• Ability to work directly with US-based customers and business stakeholders.

• Ability to explain complex AI/ML and Generative AI concepts to technical and non-technical audiences.

• Experience managing technical discussions, solution reviews, and delivery expectations.

Key Technical Skills

• Python

• Machine Learning and Deep Learning

• TensorFlow / PyTorch

• Natural Language Processing (NLP)

• Generative AI and Large Language Models (LLMs)

• Prompt Engineering

• Retrieval-Augmented Generation (RAG)

• Embeddings and Semantic Search

• Vector Databases

• LLM Orchestration and Evaluation

• Agentic AI and AI Assistants

• REST APIs and Microservices

• Model Deployment and Monitoring

• CI/CD and GitHub Actions

• Docker and Containerization

• JavaScript / Node.js / TypeScript

Preferred Industry Experience

• Banking, Financial Services, and Insurance (BFSI).

• Property and Casualty (P&C) Insurance.

• Guidewire-based insurance platforms.

• High-volume enterprise application environments.