Location: Any location in INDIA

We are seeking an experienced Senior AI/ML Engineer to lead the end-to-end design, development, and deployment of predictive models, deep learning architectures, and applied Generative AI solutions.

In this role, you will take ownership of the modeling lifecycle: performing rigorous exploratory data analysis (EDA), writing distributed feature pipelines, training high-accuracy classification and forecasting models using semi-structured datasets, and packaging models into production-ready Python services. You will build and manage ML pipelines using Azure Machine Learning (Workspaces, Pipelines, and Endpoints) while collaborating cross-functionally with data engineering, DevOps, and product teams to integrate AI capabilities into broader enterprise platforms.

Relevant experience level: 6 - 8 Years

Core Stack:

- Languages: Python (Advanced/Must-have), PySpark (Must-have), SQL; R (Good-to-have)
- ML & Deep Learning: Scikit-learn, PyTorch, TensorFlow, XGBoost/LightGBM
- Cloud & MLOps Infrastructure: Azure Machine Learning (Workspaces, Experiments, Pipelines, Model Registry, Managed Endpoints), MLflow
- Generative AI: Large Language Models (LLMs), Azure OpenAI, Retrieval-Augmented Generation (RAG)
- Data Processing: Semi-structured data parsing (JSON, Parquet, Avro, XML), Databricks / Spark

Roles & Responsibilities

Design, train, and evaluate machine learning and deep learning models for a Data Quality (DQ) and forecasting use case. Own core modeling and data science work: traditional ML, deep learning, and GenAI, not CI/CD pipeline ownership or production orchestration (this sits with a separate MLOps function). Build models that are accurate, well-validated, and ready for handoff into the production environment.

Key Responsibilities

1. Machine Learning & Deep Learning Engineering

- Design, train, and validate traditional ML and deep learning models,



with a dedicated focus on classification, anomaly detection, and time-series forecasting.
- Handle and process semi-structured and unstructured datasets (e.g., JSON logs, nested payloads, event streams) alongside structured tabular data.
- Architect and implement neural networks and classical algorithms using PyTorch, TensorFlow, and Scikit-learn.
- Execute structured experimentation, hyperparameter tuning, and A/B validation to optimize model accuracy, inference latency, and memory footprint.

2. Service Architecture & Azure ML Deployment

- Design, build, and maintain production-quality Python services and APIs supporting the complete AI lifecycle (data ingestion, offline training, validation, real-time/batch inference).
- Leverage Azure Machine Learning to build reproducible training pipelines, track runs in experiment registries, package artifacts, and deploy to secure, scalable managed endpoints.
- Implement responsible AI practices, monitor deployed model health, track drift (data and concept drift), and manage retraining workflows post-deployment.

3. Generative AI & Advanced Solutions

- Design and implement Generative AI capabilities, including LLM-powered applications and Retrieval-Augmented Generation (RAG) patterns.
- Develop contextual data retrieval, prompt templates, and evaluation loops for enterprise use cases.

4. Engineering Standards & Cross-Functional Collaboration

- Collaborate closely with data engineering, platform, and DevOps teams to integrate ML solutions into CI/CD pipelines and enterprise architectures.




- Conduct comprehensive code reviews, establish team best practices for model testing/linting, and mentor junior engineers.
- Maintain transparent documentation for system architectures, data pipelines, model benchmarks, and experiment trade-offs for technical and non-technical stakeholders.

Minimum qualifications

- 6 - 8 years of hands-on experience building, validating, and deploying machine learning and deep learning models into production systems.
- Must-have modeling expertise: Proven track record delivering classification and forecasting models, specifically working with semi-structured data (nested JSON, log events, schema-on-read formats).
- Programming Mastery: Advanced proficiency in Python and solid hands-on experience with PySpark for distributed data transformation and EDA.
- Framework Depth: Strong command of core ML/DL frameworks: Scikit-learn, PyTorch, or TensorFlow.
- Cloud ML Platform: Deep hands-on experience with the Azure Machine Learning Workspace ecosystem (Azure ML Pipelines, Model Registry, Experiments, and Managed Online/Batch Endpoints).
- Applied GenAI: Practical experience designing LLM-based solutions, including RAG architecture and embedding-based retrieval.
- Data Handling: Strong analytical skills in SQL and handling high-volume feature pipelines.
- Communication & Delivery: Demonstrated ability to articulate technical tradeoffs, system limitations, and business impact clearly across engineering and business teams.

Preferred Skills

- Working proficiency or prior project experience with R.
- Experience with Databricks, Delta Lake, and Lakehouse architecture.
- Understanding of automated CI/CD integration with Azure DevOps/GitHub Actions for ML artifacts.

Microsoft certifications: Azure Data Scientist Associate (DP-100) or Azure AI Engineer Associate (AI-102)

📌 AI & Data Science Engineer (Noida)
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