Pune, Bengaluru · Hybrid

AI/ML Engineer

Full-timePosted 12 Nov

Salary: Not disclosed
Experience: 2–6 years
Eligible batches: 2020 – 2024
Degree: Bachelor's
Location: Pune, BengaluruHybrid
Apply by: 20 Nov 2026

About the role

Rockwell Automation is seeking an experienced AI/ML Engineer to create, scale, and operate intelligent solutions that address real business challenges. The position is hybrid, based in Pune and Bengaluru, and involves end‑to‑end work on generative AI, large language models, retrieval‑augmented generation pipelines, and traditional machine‑learning systems. You will collaborate with data scientists, product owners, and platform engineers to turn research prototypes into reliable production services.

Responsibilities

Design and implement LLM‑driven applications, RAG pipelines, and autonomous agent workflows that automate business processes.
Stay abreast of the latest research in generative AI, evaluate its relevance, and integrate promising techniques.
Document architectures, experiments, and decisions while contributing to internal ML standards and code reviews.
Embed responsible‑AI safeguards such as fairness, explainability, privacy, and security throughout model development.
Build and maintain scalable data pipelines for feature engineering, model training, and real‑time inference.
Deploy models using MLOps best practices, including CI/CD, containerization, versioning, and automated retraining.
Monitor production models for drift, bias, and performance issues, and trigger retraining or alerts as needed.
Partner with data engineers to ensure high‑quality, governed data is available for modeling.
Work with product managers to translate business requirements into feasible AI solutions and communicate model limitations.
Run experiments, A/B tests, and offline evaluations to measure impact and refine models.

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Eligibility

Bachelor’s degree in Computer Science, Data Science, Statistics, Mathematics, Machine Learning or a related discipline, or equivalent practical experience.
2 – 5+ years of hands‑on experience building and deploying production‑grade machine‑learning models.

Skills

Strong Python programming and SQL knowledge.
Proficiency with TensorFlow, PyTorch, scikit‑learn, XGBoost or LightGBM.
Experience with the full ML lifecycle, including data preprocessing, feature engineering, training, hyper‑parameter tuning, evaluation, and deployment.
Hands‑on use of MLOps tools such as MLflow, Kubeflow, SageMaker, Vertex AI, or Airflow.
Familiarity with containerization (Docker) and orchestration (Kubernetes).
Working knowledge of distributed processing frameworks like Spark.
Experience serving models via APIs (FastAPI, Flask, TorchServe, Triton).
Practical exposure to LLM APIs (OpenAI, Anthropic, Llama, Mistral, Gemini) and frameworks like LangChain or LlamaIndex.
Understanding of vector databases (Pinecone, Weaviate, FAISS, Milvus, Chroma) for semantic retrieval.
Awareness of responsible‑AI practices, model interpretability, and privacy‑preserving techniques.