Role Overview : This role requires a combination of advanced data science expertise, strong system design capabilities, and hands-on technical leadership. You will drive architectural decisions, guide teams through complex ML implementation challenges, and partner with business, data engineering, DevOps, cloud, and governance stakeholders to deliver production-grade AI/ML capabilities.Key Responsibilities : - Lead the architecture and implementation of end-to-end ML solutions, covering data ingestion, feature engineering, training, validation, deployment, inference, monitoring, and lifecycle management.- Translate business objectives and technical requirements into scalable ML solution designs, selecting appropriate algorithms, infrastructure, deployment patterns, and operational approaches.- Develop and optimize advanced ML models for classification, regression, forecasting, anomaly detection, and complex business use cases.- Design enterprise-grade ML pipeline architectures supporting reusable components, parallel execution, distributed processing, automated orchestration, and high-volume workloads.- Establish standards for Python application architecture, reusable libraries, API contracts, configuration management, testing, code quality, and production readiness.- Define and drive MLOps strategy, including experiment tracking, model registries, CI/CD, automated validation, release management, deployment approvals, rollback, and reproducibility.- Architect batch and real-time inference solutions, considering throughput, latency, availability, scalability, fault tolerance, and infrastructure costs.- Design comprehensive model observability frameworks covering model performance, data quality, data and model drift, service health, latency, and operational alerts.- Establish automated retraining and model promotion frameworks with appropriate validation gates, human oversight,



and governance controls.- Drive performance engineering and cost optimization across data processing, model inference, compute resources, and cloud infrastructure.- Define reference architectures and reusable ML engineering frameworks that accelerate solution delivery and standardize implementation across teams.- Evaluate and guide the adoption of cloud ML platforms, distributed computing technologies, feature stores, containerization, orchestration, and model serving frameworks.- Partner with data engineering, application engineering, DevOps, security, and cloud architecture teams to ensure seamless integration with enterprise platforms.- Lead technical reviews, architecture discussions, code reviews, and root-cause analysis for complex production incidents.- Mentor data scientists and ML engineers, establish engineering best practices, and guide teams on model development, productionization, and operational excellence.- Ensure compliance with enterprise security, privacy, model governance, explainability, auditability, and responsible AI requirements.- Engage with business and technology stakeholders to communicate solution trade-offs, delivery risks, performance outcomes, and technical recommendations.Required Skills & Expertise : - Extensive hands-on experience in Python, SQL, ML model development, and production software engineering.- Proven expertise in architecting and deploying end-to-end ML solutions in production environments.- Robust understanding of system design, distributed processing, scalable pipeline architectures, APIs,



batch processing, and real-time inference.- Deep experience implementing MLOps frameworks, including experiment tracking, model registries, CI/CD, automated testing, deployment automation, and model lifecycle management.- Strong proficiency in designing reliable, reusable, configurable, and observable ML pipelines.- Experience implementing model monitoring, drift detection, automated validation, retraining strategies, and production incident management.- Demonstrated ability to optimize processing performance, inference latency, resource utilization, and cloud infrastructure costs.- Experience with cloud ML platforms such as Azure ML, AWS SageMaker, GCP Vertex AI, or Databricks.- Robust knowledge of Docker, Kubernetes, Spark/PySpark, workflow orchestration, and cloud-native deployment patterns.- Experience establishing technical standards, reviewing solution architectures, and guiding engineering teams.- Excellent stakeholder management, communication, problem-solving, and technical decision-making skills.Preferred Qualifications : - Experience building enterprise ML platforms, shared ML frameworks, feature stores, or reusable model-serving infrastructure.- Exposure to streaming architectures, distributed inference, model serving optimization, and high-availability production systems.- Experience in banking, lending, credit risk, fraud detection, financial crime, or other regulated financial services use cases.- Strong understanding of model explainability, responsible AI, model risk management, audit trails, and regulatory governance.- Experience working with cross-functional or geographically distributed teams and influencing technical strategy.- Bachelor's or master's degree in Computer Science, Data Science, Engineering, Mathematics, Statistics, or a related discipline. (ref:hirist.tech)

📌 Incedo - Lead Data Scientist - ML Engineering (Gurugram)
🏢 Incedo Technology Solutions
📍 Gurugram