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AI/ML engineer with 8+ years of experience building scalable NLP, Machine Learning, and Generative AI solutions. I specialize in LLM-based systems, RAG, embeddings/vector search, and agentic workflows deployed on cloud platforms (Azure/AWS/GCP) with strong attention to model evaluation and business impact. I’ve led end-to-end delivery of LLM/NLG product features, built prompt/RAG pipelines and semantic AI services, and integrated AI assistants into enterprise platforms to improve productivity and decision-making. I’m available to start immediately and bring strong engineering practices across Python, APIs, microservices, and CI/CD.…AI/ML engineer with 8+ years of experience building scalable NLP, Machine Learning, and Generative AI solutions. I specialize in LLM-based systems, RAG, embeddings/vector search, and agentic workflows deployed on cloud platforms (Azure/AWS/GCP) with strong attention to model evaluation and business impact. I’ve led end-to-end delivery of LLM/NLG product features, built prompt/RAG pipelines and semantic AI services, and integrated AI assistants into enterprise platforms to improve productivity and decision-making. I’m available to start immediately and bring strong engineering practices across Python, APIs, microservices, and CI/CD.

Anju Balachandran

AI Engineer, Data Scientist, AI Developer

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AI/ML engineer with 8+ years of experience building scalable NLP, Machine Learning, and Generative AI solutions. I specialize in LLM-based systems, RAG, embeddings/vector search, and agentic workflows deployed on cloud platforms (Azure/AWS/GCP) with strong attention to model evaluation and business impact. I’ve led end-to-end delivery of LLM/NLG product features, built prompt/RAG pipelines and semantic AI services, and integrated AI assistants into enterprise platforms to improve productivity and decision-making. I’m available to start immediately and bring strong engineering practices across Python, APIs, microservices, and CI/CD.…AI/ML engineer with 8+ years of experience building scalable NLP, Machine Learning, and Generative AI solutions. I specialize in LLM-based systems, RAG, embeddings/vector search, and agentic workflows deployed on cloud platforms (Azure/AWS/GCP) with strong attention to model evaluation and business impact. I’ve led end-to-end delivery of LLM/NLG product features, built prompt/RAG pipelines and semantic AI services, and integrated AI assistants into enterprise platforms to improve productivity and decision-making. I’m available to start immediately and bring strong engineering practices across Python, APIs, microservices, and CI/CD.

Available to hire

AI/ML engineer with 8+ years of experience building scalable NLP, Machine Learning, and Generative AI solutions. I specialize in LLM-based systems, RAG, embeddings/vector search, and agentic workflows deployed on cloud platforms (Azure/AWS/GCP) with strong attention to model evaluation and business impact.

I’ve led end-to-end delivery of LLM/NLG product features, built prompt/RAG pipelines and semantic AI services, and integrated AI assistants into enterprise platforms to improve productivity and decision-making. I’m available to start immediately and bring strong engineering practices across Python, APIs, microservices, and CI/CD.

Skills

Experience Level

Expert

Expert

Expert

Expert

Expert

Expert

Expert

Expert

Expert

Intermediate

Intermediate

Intermediate

Intermediate

Intermediate

Intermediate

Intermediate

Intermediate

Language

English

Fluent

Malayalam

Fluent

German

Intermediate

Work Experience

Senior Data Scientist at IBM

January 1, 2024 - October 2, 2025

Built and deployed an Azure OpenAI-powered outlet assistant integrated into Imperial Brands’ internal 360 Connect platform for field sales representatives across the German market. Worked in a Scrum team of 7 to deliver actionable insights and sales summaries to support more effective outlet visits. Reduced pre-visit preparation time by 40%, increased field-force capacity by 3.75%, and enabled additional outlet visits. Collaborated with business stakeholders to understand key business rules and implemented prompt workflows and AI pipelines in Azure ML to extract insights from sales data using Python, Azure OpenAI, Azure ML, Azure SQL, vector database, FAISS, and agentic/LLM approaches.

Machine Learning Engineer (Data Analyst – AI/ML)

September 1, 2018 - September 1, 2019

Built an NLP-based grammar/format extraction system for Unilever to automatically identify product size information from high-volume product descriptions. Implemented text processing pipelines with domain-specific rules and semantic modeling to process 1,000+ product descriptions weekly with 90–94% accuracy, reducing manual effort by 60%. Tech stack included Python, NLTK/CoreNLP, regex, ontologies, knowledge graphs, Apache Jena, and Protégé.

Senior Machine Learning Engineer (AI Specialist Product Engineering) at LTIMindtree

April 1, 2018 - December 1, 2023

Led end-to-end delivery of NLG product features using Agile practices and cross-functional coordination. Developed and deployed LLM-powered text generation and summarization systems for enterprise applications. Built Flask-based AI microservices exposing REST APIs for LLM-driven text generation and summarization. Developed an NLG pipeline converting analytical outputs from various charts into automated business narratives. Designed and implemented a classification module (ML-based) to categorize user questions into distinct classes for improved predictions. Tech stack included Python, LangChain, GPT-4/LLMs, AWS, NLTK, WordNet, SVM/Decision Tree, Flask, REST APIs.

Natural Language Processing Engineer at Zwya m

June 1, 2017 - August 1, 2018

Developed NLP pipelines for an enterprise recruitment automation platform (ATS) adopted by 350+ enterprise customers. Automated resume and JD processing using rule-based parsing, reducing processing time by ~40%. Built an SVM-based job classification system improving accuracy over the legacy rule-based approach. Implemented ontology-based candidate profiling using Apache Jena to improve skill/experience matching and job attribute alignment. Tech stack included Python, scikit-learn, SVM, NLTK/CoreNLP, ontologies, knowledge graphs, Apache Jena, and Protégé.