DE&A - AIML - Machine Learning - Python
This role focuses on developing and deploying machine learning models using Python at Zensar, leveraging data engineering and analytics skills. Candidates will work on end-to-end AI solutions, collaborating with cross-functional teams to deliver data-driven insights.
Skills required
About this role
AI/ML Engineer
Role Overview Responsible for building prescriptive and generative AI components that translate model outputs into actionable business recommendations. Bridges the gap between data science outputs and production-ready business deliverables.
Experience Required: 3–5 years
Key Responsibilities
Build prescriptive and optimisation engines using mathematical programming and heuristic solvers
Design, develope and implement LLM solutions within secure enterprise infrastructure
Engineer LLM prompts to generate plain-language business narratives and ranked recommendations
Build automated report generation pipelines delivering structured outputs to business consumers
Integrate ML model outputs with generative AI components for consistent and explainable end-to-end delivery
Build rule-based trigger engines and exception management workflows
Support A/B testing frameworks to compare AI-driven vs. baseline recommendations
Design and implement impact analysis and counterfactual simulation capabilities
Required Skills
3–5 years of experience in AI/ML engineering, software engineering, or applied AI development
Experience with mathematical optimisation (PuLP, OR-Tools, linear programming)
Proficiency in LLM prompt engineering and self-hosted or API-based model deployment
Familiarity with LLM frameworks (LangChain, Hugging Face, OpenAI API)
Strong Python skills and experience building automated data and reporting pipelines
Knowledge of NLP and generative AI techniques
Experience with supply chain or inventory optimisation preferred
Responsibilities
AI/ML Engineer
Role Overview Responsible for building prescriptive and generative AI components that translate model outputs into actionable business recommendations. Bridges the gap between data science outputs and production-ready business deliverables.
Experience Required: 3–5 years
Key Responsibilities
Build prescriptive and optimisation engines using mathematical programming and heuristic solvers
Design, develope and implement LLM solutions within secure enterprise infrastructure
Engineer LLM prompts to generate plain-language business narratives and ranked recommendations
Build automated report generation pipelines delivering structured outputs to business consumers
Integrate ML model outputs with generative AI components for consistent and explainable end-to-end delivery
Build rule-based trigger engines and exception management workflows
Support A/B testing frameworks to compare AI-driven vs. baseline recommendations
Design and implement impact analysis and counterfactual simulation capabilities
Required Skills
3–5 years of experience in AI/ML engineering, software engineering, or applied AI development
Experience with mathematical optimisation (PuLP, OR-Tools, linear programming)
Proficiency in LLM prompt engineering and self-hosted or API-based model deployment
Familiarity with LLM frameworks (LangChain, Hugging Face, OpenAI API)
Strong Python skills and experience building automated data and reporting pipelines
Knowledge of NLP and generative AI techniques
Experience with supply chain or inventory optimisation preferred
Qualifications
AI/ML Engineer
Role Overview Responsible for building prescriptive and generative AI components that translate model outputs into actionable business recommendations. Bridges the gap between data science outputs and production-ready business deliverables.
Experience Required: 3–5 years
Key Responsibilities
Build prescriptive and optimisation engines using mathematical programming and heuristic solvers
Design, develope and implement LLM solutions within secure enterprise infrastructure
Engineer LLM prompts to generate plain-language business narratives and ranked recommendations
Build automated report generation pipelines delivering structured outputs to business consumers
Integrate ML model outputs with generative AI components for consistent and explainable end-to-end delivery
Build rule-based trigger engines and exception management workflows
Support A/B testing frameworks to compare AI-driven vs. baseline recommendations
Design and implement impact analysis and counterfactual simulation capabilities
Required Skills
3–5 years of experience in AI/ML engineering, software engineering, or applied AI development
Experience with mathematical optimisation (PuLP, OR-Tools, linear programming)
Proficiency in LLM prompt engineering and self-hosted or API-based model deployment
Familiarity with LLM frameworks (LangChain, Hugging Face, OpenAI API)
Strong Python skills and experience building automated data and reporting pipelines
Knowledge of NLP and generative AI techniques
Experience with supply chain or inventory optimisation preferred
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