AI/ML Engineer

About the Role

We are looking for a highly motivated AI & Machine Learning Engineer with strong expertise in modern AI technologies, including Large Language Models (LLMs). The role focuses on end-to-end AI and ML development, including designing, building, and deploying intelligent applications that leverage machine learning, LLMs, and retrieval-augmented generation (RAG) techniques.

The ideal candidate demonstrates strong Python development skills, hands-on experience working with LLMs, prompt engineering, and RAG-based application development, along with solid analytical thinking and the ability to communicate complex concepts effectively to both technical teams and business stakeholders.

Key Responsibilities

AI / ML & Document Intelligence

- Design and implement AI/ML solutions for document abstraction, classification, and information extraction from structured and unstructured documents (PDFs, scanned files, Office documents).

- Develop and optimize NLP, OCR, and transformer-based models for entity extraction, classification, and summarization.

- Build end-to-end document processing pipelines integrating OCR, ML inference, and post-processing rules.

- Continuously evaluate and improve model accuracy, performance, and reliability.

Software Engineering

- Develop clean, efficient, and scalable Python code following engineering best practices.

- Build and maintain RESTful APIs and microservices (e.g., FastAPI or similar frameworks).

- Ensure code quality through unit testing, logging, error handling, and observability.

- Collaborate with frontend teams (Angular/SPA or equivalent) for AI service integration.

Analytical & Problem Solving

- Analyze complex document layouts, content variability, and edge cases.

- Perform data exploration, feature engineering, and root-cause analysis of model failures.

- Translate business requirements and document rules into effective ML-based solutions.

Collaboration & Communication

- Work closely with product managers, architects, data engineers, and business stakeholders.

- Clearly explain AI model behavior, outputs, trade-offs, and limitations to non-technical audiences.

- Contribute to design reviews, sprint planning, demos, and technical documentation.

Required Qualifications

Technical Skills

- Strong proficiency in Python for data processing, ML development, and API building.

- Strong knowledge and hands-on experience with Large Language Models (LLMs).

- Experience building applications using Retrieval-Augmented Generation (RAG) patterns.

- Hands-on experience with prompt engineering and optimizing LLM outputs.

- Solid understanding of Machine Learning, NLP, and information extraction techniques.

- Hands-on experience with ML frameworks and libraries such as PyTorch, TensorFlow, scikit-learn, spaCy, Transformers, or equivalent.

- Experience working with OCR and document AI workflows.

- Familiarity with cloud-native development and REST-based architectures.

- Working knowledge of databases (SQL/NoSQL) and data pipelines.

Analytical Skills

- Strong analytical thinking and structured problem-solving ability.

- Ability to understand and model complex document structures and patterns.

- Experience measuring model performance using both technical and business-oriented metrics.

Communication & Professional Skills

- Strong verbal and written communication skills.

- Ability to collaborate effectively in cross-functional teams.

- Comfortable presenting technical ideas and AI outcomes to diverse audiences.

Preferred / Nice to Have

- Experience with cloud AI platforms (Azure, AWS, or GCP).

- Exposure to MLOps, model deployment, monitoring, and lifecycle management.

- Familiarity with vector databases and embedding-based search.

- Familiarity with security, authentication, and enterprise data governance standards.

- Experience working in Agile / Scrum environments.