Leads the design, development, deployment, and operation of enterprise-scale AI, machine learning, NLP, GenAI, and agentic AI solutions. Builds scalable Python pipelines on Databricks and Azure, develops distributed ML applications, and implements MLOps, LLMOps, CI/CD, and responsible AI practices. Collaborates with technical and business teams to deliver actionable intelligence, establishes AI strategies and roadmaps, mentors junior engineers, and supports team scaling.
Duties & Responsibilities
Hands-on experience in designing, developing and deploying AI/ML/NLP/GenAI/Agentic AI solutions (involving modeling, benchmarking and inferencing) at enterprise scale for e-commerce.
Hands-on experience in building, evaluating and deploying AI solutions leveraging LLMs, vector DBs, RAG, Agentic frameworks (such as LangChain, HayStack, Semantic Kernel).
Strong programming skills in Python (experience with PyTorch, TensorFlow, scikit-learn, etc.) and Solid understanding of ML algorithms, NLP, computer vision, finetuning LLMs.
Stay updated on advancements in AI/ML, LLMs, and agentic frameworks to propose innovative modular/reusable solutions/approaches/pipelines to enable the Platforms & Innovation Lab pillar.
Collaborate with architects, data engineers, AI engineers, product and business teams to translate business requirements into fully operational AI solutions.
Analyzing large volumes data and establishing AI/ML/NLP and Generative AI based automation workflows to enable cross functional business teams with real-time actionable intelligence.
Building modular, reliable, scalable python-based pipelines on Databricks and Azure with attention to security, cost, and data sensitivity.
Developing/implementing algorithms and maintaining systems that deals with streaming big data for AI/ML based actionable insights.
Drive all phases of software development including design, prototyping, and incremental production release adhering to industry best DevOps, MLOps, LLMOps practices.
Designing and implementing highly reliable, fault-tolerant distributed ML applications with focus on ML Operations and CI/CD.
Implement responsible AI practices and templates, focusing model explainability, fairness, Human-In-The-Loop validation, compliance and governance.
Provide technical leadership to junior AI engineers. Actively participate and lead Team scaling efforts.
Requirements
Basic Qualifications
Bachelor’s degree in computer science, machine intelligence and 6+ years of experience
Solid (6+ years) hands-on experience in ML training/inferencing pipelines
Proven track record (e.g., 5–8+ years for Lead roles) in Machine Learning/AI engineering.
Strong programming skills (Python, PyTorch/TensorFlow, scikit-learn).
Knowledge of Machine Learning algorithms, NLP, Deep Learning, LLMs.
Experience with data pipelines, model training, and deployment.
Familiarity with Databricks and one cloud platforms (AWS, Azure, GCP).
Ability to work both independently and as part of a collaborative team
Preferred Qualifications
Masters/PhD in the field computer science, machine intelligence and 4+ years of experience
Hands-on experience in designing, developing and deploying AI/ML/NLP/GenAI/Agentic AI solutions (involving modeling, benchmarking, inferencing) at enterprise scale for e-commerce.
Hands-on experience in building, evaluating and deploying AI solutions leveraging LLMs, vector DBs, RAG, Agentic frameworks (such as LangChain, HayStack, Semantic Kernel).
Strong programming skills in Python (experience with PyTorch, TensorFlow, scikit-learn, etc.) and Solid understanding of ML algorithms, NLP, computer vision, finetuning LLMs.
Proven experience in defining AI strategy and roadmaps.
Experience with agentic frameworks (LangChain, Semantic Kernel, Haystack).
Expertise in vector databases (FAISS, Pinecone, Weaviate) and MLOps, LLM Ops.
Track record of mentoring and scaling AI teams.
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