AI/ML Engineer

AI/ML Engineer

• We are looking for an experienced AI/ML Engineer to join our AI Engineering team and build intelligent, scalable, and production-ready AI/ML solutions.
• The role will focus on designing, developing, deploying, and optimizing
• Machine Learning models and Agentic AI solutions that deliver measurable business value across customer-facing and operational workflows.
• The ideal candidate should have strong hands-on experience in
• Python, Machine Learning, SQL, MLOps, and Agentic AI frameworks, along with the ability to translate business requirements into scalable AI solutions.

About Sonata Software

• LangGraph, LangChain, AutoGen, CrewAI
• About Sonata Software

Sonata Software is an

AI-first modernization engineering company that helps enterprises transform legacy systems into intelligent, scalable business platforms.

Powered by its

Harmoni.AI platform, Sonata delivers AI-led modernization across cloud, data, AI, Dynamics, test automation, and managed services.

Headquartered in Bengaluru, India, Sonata has more than $1.2 billion in revenue and 6,400+ AI engineers supporting global delivery across regions including the US, UK, India, Malaysia, Mexico, Australia, DACH, and the Nordics.

With deep partnerships across

• Microsoft, AWS, Salesforce, and Snowflake, Sonata helps Fortune 500 enterprises accelerate innovation, improve efficiency, and drive sustainable growth.
• www.sonata-software.com m

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 covering 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 develop 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.
• 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/ML workloads on cloud platforms.
• 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.

Requirements

• 6–8 Years
• Python, SQL, Machine Learning, ML Modelling, Agentic AI
• 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 LangGraph, LangChain, AutoGen, CrewAI, or similar frameworks.
• Strong understanding of supervised and unsupervised learning techniques.
• Experience with recommendation systems, predictive analytics, forecasting, classification, anomaly detection, or optimization.

Hands-on experience with

• Scikit-Learn, XGBoost, LightGBM, TensorFlow, PyTorch, or equivalent 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 enterprise systems through APIs and databases.
• Docker, CI/CD, Git, and modern software engineering practices.
• cloud platforms.
• Strong analytical, problem-solving, and communication skills.
• Experience with optimization techniques, routing algorithms, scheduling, or Operations Research.
• Knowledge of demand forecasting, customer propensity modelling, pricing analytics, and recommendation engines.
• Experience with explainable AI, model evaluation frameworks, and experimentation methodologies.

Data & Analytics

• Snowflake, Databricks, or modern cloud data platforms.
• Experience building analytical dashboards and decision-support solutions.
• Familiarity with large-scale data processing and distributed computing.
• Exposure to
• Exposure to
• Programming
• Python, SQL
• ML Frameworks
• Generative AI
• LLMs, RAG, Vector Databases
• Data
• MLOps

Preferred

• AWS or Azure

Generative AI

• LLMs, RAG architectures, vector databases, and Agentic AI frameworks.
• Experience integrating ML solutions with Generative AI applications.
• Domain Knowledge
• Sales, Pricing, Customer Intelligence, E-commerce, Distribution, Logistics, Supply Chain, or ERP/CRM ecosystems is preferred.
• Scikit-Learn, XGBoost, LightGBM, TensorFlow, PyTorch
• MLflow, Docker, CI/CD, Model Monitoring
• Platformation™ framework

Agentic AI

Snowflake, SQL, APIs, Data Pipelines

Cloud

AWS, Azure