Digital Senior Engineer

Job Description

AI/ML Engineer

Location: Pune | Hybrid

Experience: 6-8 years

Primary Skill: Python, SQL, ML Modelling, Agentic AI

Our Objective

We are building AI-powered solutions that help businesses improve customer outcomes, operational efficiency, revenue growth, and decision-making through the practical application of Machine Learning and AI.

As part of the AI Engineering team, you will work on the design, development, deployment, and optimization of ML-driven solutions that deliver measurable business value across customer-facing and operational workflows.

Key Responsibilities

Machine Learning Solution Development

• Design, develop, and deploy Machine Learning models for prediction, recommendation, optimization, classification, and forecasting use cases.
• Build scalable ML pipelines for data preparation, feature engineering, model training, evaluation, and deployment.
• Apply statistical and machine learning techniques to solve business problems using structured and semi-structured data.
• Work closely with product, engineering, and business teams to translate requirements into production-ready AI/ML solutions.
  
  

Agentic AI Development

• Design and build agentic workflows using frameworks such as LangGraph, LangChain, AutoGen, CrewAI, or similar.
• Develop AI agents capable of reasoning, task orchestration, tool usage, and multi-step workflow execution.
• Integrate AI agents with enterprise systems, APIs, databases, and business applications.
• Collaborate with AI engineers to combine Agentic AI capabilities with predictive and analytical ML models.
  
  

Data Engineering & Integration

• Build and maintain data pipelines to ingest, transform, and process data from enterprise systems, APIs, databases, and external sources.
• Develop reusable data services and ML components to accelerate solution delivery.
• Ensure data quality, reliability, and scalability for model development and production workloads.
  
  

MLOps & Productionization

• Implement CI/CD pipelines for ML models and AI services.
• Establish model monitoring, performance tracking, retraining, and deployment processes.
• Manage model lifecycle, experimentation, versioning, and governance.
• Support deployment of AI and ML workloads on cloud platforms.
  
  

Engineering Excellence

• Follow best practices for software engineering, testing, observability, and documentation.
• Leverage AI-assisted development tools to improve engineering productivity.
• Contribute to reusable frameworks, standards, and best practices across the AI team.
  
  

Required Qualifications

• 6-8 years of software engineering experience with strong Python development skills.
• 3+ years of hands-on experience building and deploying Machine Learning solutions.
• Hands-on experience building Agentic AI solutions using frameworks such as LangGraph, LangChain, AutoGen, CrewAI, or similar.
• Strong understanding of supervised and unsupervised learning techniques.
• Experience with recommendation systems, predictive analytics, forecasting, classification, anomaly detection, or optimization problems.
• Hands-on experience with Scikit-Learn, XGBoost, LightGBM, TensorFlow, PyTorch, or equivalent ML frameworks.
• Strong SQL and data analysis skills.
• Experience with feature engineering, model evaluation, and experimentation frameworks.
• Familiarity with MLOps practices, model deployment, monitoring, and lifecycle management.
• Experience building data pipelines and integrating with enterprise systems through APIs and databases.
• Experience with Docker, CI/CD pipelines, Git, and modern software engineering practices.
• Experience working with AWS or Azure cloud platforms.
• Strong analytical, problem-solving, and communication skills.
  
  

Good to Have

Advanced AI & Data Platforms

• Experience with optimization techniques, routing algorithms, scheduling, or Operations Research.
• Knowledge of demand forecasting, customer propensity modeling, pricing analytics, and recommendation engines.
• Experience with explainable AI, model evaluation frameworks, and experimentation methodologies.
  
  

Data & Analytics

• Knowledge of Operations Research, routing algorithms, scheduling, or decision optimization techniques.
• Experience with demand forecasting, pricing analytics, customer intelligence, propensity modeling, and recommendation engines.
• Experience with Snowflake, Databricks, or modern cloud data platforms.
• Experience building analytical dashboards and decision-support solutions.
• Familiarity with large-scale data processing and distributed computing.
  
  

Generative AI

• Exposure to LLMs, RAG architectures, vector databases, and agentic frameworks.
• Experience integrating ML solutions with GenAI applications.
  
  

Domain Knowledge

• Exposure to sales, pricing, customer intelligence, e-commerce, distribution, logistics, supply chain, or ERP/CRM ecosystems.
  
  

Technology Stack

• Languages: Python, SQL
• ML Frameworks: Scikit-Learn, XGBoost, LightGBM, TensorFlow, PyTorch
• Agentic AI: LangGraph, LangChain, AutoGen, CrewAI
• Data: Snowflake, SQL, APIs, Data Pipelines
• MLOps: MLflow, Docker, CI/CD, Model Monitoring
• Cloud: AWS or Azure
• Development Tools: GitHub, Azure DevOps, GitLab, GitHub Copilot, Cursor