Job highlights

- Experience Level Total experience Relevant experience (must) ML Engineer 6-10 years 3+ years applied ML / entity resolution
- Develop propensity,engagement and scoring models . Productionise models as batch pipelines
- Certifications: AWS Certified Machine Learning Engineer / Specialty (plus)

About Us We are growing our Data & AI practice, which delivers cloud data platforms, data modernisation and migration, analytics and AI-enabled engineering for global enterprise clients. You will work in cross-functional teams on long-term engagements, using modern cloud technologies and AI-assisted engineering practices. Location: Offshore (India) Full time.

Role Summary

Builds and runs production ML on customer data - identity resolution, propensity and scoring models - with MLOps discipline.

Experience Level Total experience Relevant experience (must) ML Engineer 6-10 years 3+ years applied ML / entity resolution; 2+ years production ML on AWS Roles & Responsibilities

- Build identity resolution / record matching (deterministic and probabilistic)
- Develop propensity, engagement and scoring models
- Productionise models as batch pipelines; set up registry, monitoring and retraining




- Measure model quality (precision / recall, drift) and explain results to business
- Work with data engineers on features, performance and cost

Mandatory Skills
- Python, PySpark, statistics
- Entity resolution tools (Splink, Zingg, AWS Entity Resolution or equivalent)
- ML on AWS (SageMaker or Spark ML)
- MLOps tooling (MLflow or equivalent)

Nice to Have
- Marketing analytics / lead scoring
- MDM
- Feature stores
- GenAI / LLM applications

Also Expected Clear communication with remote client stakeholders across time zones; Comfortable using AI coding and productivity tools, and reviewing AI-generated work critically; Agile ways of working; ownership and accountability. Certifications: AWS Certified Machine Learning Engineer / Specialty (plus).

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