Senior AIML Engineer

Job Title

• Senior AI/ML Engineer – Machine Learning, Optimization & GenAI
• Location: Ahmedabad
• Experience: 6+ Years
• Project Duration: 4 months + possible 1–2 month extension
• Budget: Up to ₹2 Lakh/month
• Work Mode: Onsite / Client-facing
• Travel: Approximately 50% travel may be required
• Project Phase: Phase 1
• Technology Focus: 70% Classical ML / Modelling / Statistics + 30% LLM / GenAI

Role Overview

We are looking for a Senior AI/ML Engineer / Data Scientist with strong hands-on experience in Classical Machine Learning, Statistical Modelling and Optimization, along with working knowledge of LLMs and Generative AI.

The primary focus of this role will be developing ML and optimization solutions to address complex business problems. LLM/GenAI will form a secondary component of the role. Deep MCP Server or Agentic AI expertise is not a primary requirement.

Key Responsibilities

• Understand complex business problems and translate them into ML, statistical and optimization solutions.
• Design, develop and implement classical machine learning models.
• Perform statistical analysis, predictive modelling and optimization.
• Apply appropriate ML algorithms based on business objectives and data characteristics.
• Perform data exploration, feature engineering, model development and validation.
• Develop optimization models to support business decision-making.
• Evaluate model performance and improve model accuracy, robustness and scalability.
• Work with stakeholders to understand business requirements and evolving problem statements.
• Develop prototypes and convert successful analytical solutions into production-ready solutions.
• Apply LLM/Generative AI techniques to selected business use cases.
• Work with LLM APIs, prompt engineering and basic RAG/GenAI architectures where required.
• Collaborate with Data Engineers, Backend Engineers and Frontend Engineers to build end-to-end solutions.
• Support model deployment, testing, documentation and client handover.
• Participate in technical discussions and explain analytical approaches and outcomes to business stakeholders.
• Adapt quickly to changing business requirements during the project lifecycle.

• 6+ years of experience in Data Science, Machine Learning, AI/ML Engineering or a closely related field.
• Strong hands-on experience in Classical Machine Learning.
• Strong foundation in statistics, mathematics and statistical modelling.
• Strong experience in optimization techniques and modelling.
• Advanced Python programming skills.
• Strong experience with Pandas, NumPy, Scikit-learn and relevant ML libraries.
• Experience with supervised and unsupervised learning techniques.
• Strong understanding of model evaluation, validation and feature engineering.
• Experience solving real-world business problems using ML and statistical techniques.
• Working knowledge of LLMs and Generative AI.
• Experience with LLM APIs, prompt engineering and basic GenAI application development.
• Strong analytical, problem-solving and communication skills.

• Experience with Azure or other cloud platforms.
• Exposure to Azure ML or similar ML platforms.
• Experience with time-series modelling, forecasting or operations research.
• Exposure to RAG and vector databases.
• Experience integrating ML models into applications.
• Consulting or client-facing project experience.
• Experience working on optimization/business decision-support solutions.

• MCP Server experience.
• Deep Agentic AI experience.
• Multi-Agent orchestration.
• Extensive LLM architecture experience.

The ideal candidate should be strong in traditional Machine Learning, statistical modelling and optimization, with sufficient GenAI knowledge to contribute to the LLM component of the project.

Preferred Skill Balance

• 70% – Classical ML / Statistical Modelling / Optimization
• 30% – LLM / Generative AI
• Candidates who are primarily LLM/GenAI developers without strong classical ML and optimization experience should not be prioritized.

Interview Focus

Candidates Should Be Evaluated Strongly On

Machine Learning fundamentalsStatistics and statistical modellingOptimization techniquesPython and ML implementationReal-world ML problem solvingModel selection and evaluationBusiness problem → ML solution approachLLM/GenAI fundamentalsProductionization of ML solutionsClient/stakeholder communication

Skills: ml,optimization,classical,machine learning