Senior Machine Learning Engineer

Talent et au-dela; Navi Mumbai; Presencial; INR 4.000.000 - 7.000.000; Jornada completa

Descripción de la vacante

Talent et au-dela is seeking a Senior Machine Learning Engineer, AI & ML — Data Collection to lead the design, development, and deployment of a Unified AI/ML Data Collection Platform in Navi Mumbai. You will own scalable ML systems, including data ingestion, feature management, model deployment, and evaluation frameworks, with a strong focus on MLOps, observability, and cost efficiency.

You will work with ML engineers, product managers, researchers, and business stakeholders to deliver

Formación

• Bachelor’s or Master’s degree in Computer Science, Data Science, Mathematics, or a related technical field.
• 5+ years of experience in machine learning engineering or data science, focusing on ML systems, ML platforms, or distributed systems.
• Strong experience building production-grade ML systems including deployment and lifecycle management.
• Hands-on experience with MLOps tools and CI/CD, monitoring, and experiment tracking.
• Strong Python and SQL programming skills; experience with cloud platforms and containerization.

Responsabilidades

• Design and operationalize the Unified AI/ML Data Collection Platform components.
• Develop AI-driven data collection workflows across structured and unstructured sources.
• Build LLM-based workflows including RAG, prompts, extraction, summarization, and validation.
• Implement model deployment, lifecycle management, CI/CD, and model registry in production.
• Develop observability, monitoring, logging, tracing, and cost-tracking capabilities.

Conocimientos

Python

SQL

Strong communication

Educación

Bachelor’s or Master’s in CS/DS/Math

Herramientas

Docker

Kubernetes

AWS

GCP

Azure

Vector databases

LLM frameworks

Descripción del empleo

As a Senior Machine Learning Engineer, AI & ML — Data Collection, you will play a critical role in building and scaling the company’s Unified AI/ML Data Collection Platform, enabling standardized, reliable, and scalable machine learning capabilities across the organization. This role will focus on transforming existing AI/ML and LLM-driven data systems into a cohesive platform that supports data pipelines, model lifecycle management, evaluation frameworks, and production deployment.

This position requires deep hands-on expertise in machine learning engineering, LLM-based systems, ML platform development, and MLOps. You will work closely with ML engineers, product managers, researchers, and business stakeholders to deliver production-ready AI/ML systems aligned with broader business objectives and AI/ML strategy.

You will be deeply involved in the design, development, and operationalization of platform components, including data ingestion, feature management, model training and evaluation, scalable inference systems, and model observability capabilities.

You will help ensure that AI/ML systems are production-ready, observable, maintainable, and cost-efficient, with a strong emphasis on reliability, performance, governance, and developer productivity. You will leverage your expertise in areas such as large language models (LLM), retrieval-augmented generation (RAG), embeddings, vector databases, distributed systems, cloud-native architectures, and ML Operations (MLOps).

You will contribute to the end-to-end lifecycle of ML systems, from experimentation and prototyping to deployment, monitoring, optimization, and continuous improvement while mentoring engineers and promoting strong engineering practices across the team.

Team Overview

You will be part of a multidisciplinary team of ML engineers responsible for building and maintaining the Unified AI/ML Data Collection Platform. The team focuses on developing scalable systems that support data pipelines, model lifecycle management, LLM-based workflows, and evaluation frameworks, enabling downstream teams to build and deploy AI-driven data collection solutions.

Outline of Duties and Responsibilities

• AI-Powered Data Collection Systems: Design and develop scalable AI-driven data collection and enrichment workflows across structured and unstructured data sources.
• LLM & Generative AI Workflows: Build LLM-based capabilities including RAG systems, prompt orchestration, entity extraction, summarization, classification, and automated validation workflows.
• Agentic Frameworks & Model Context Integration: Design and implement agentic workflows and model-to-tool integrations that connect AI models with internal tools, APIs, knowledge stores, data sources, and workflow systems.
• Model Deployment & Lifecycle Management: Deploy, maintain, and optimize ML and LLM models in production, including model versioning, CI/CD, experiment tracking, model registry, rollout strategies, and rollback mechanisms.
• Data Quality & Evaluation: Build frameworks for evaluating extraction quality, model performance, hallucination risks, grounding, consistency, latency, coverage, and overall data reliability.
• Observability & Operational Excellence: Implement monitoring, logging, tracing, alerting, cost tracking, model performance monitoring, drift detection, and reliability dashboards for production AI/ML systems.
• Scalable Platform Engineering: Design distributed, event-driven, and cloud-native systems using asynchronous processing, message queues, containerization, and orchestration patterns to support high-volume workloads.
• Innovation & Continuous Improvement: Evaluate emerging AI/ML technologies, LLM frameworks, orchestration tools, vector databases, and model deployment approaches to improve automation capabilities and developer productivity.
• Company Values: Model company values and contribute to a culture of innovation, accountability, collaboration, inclusion, and continuous improvement.

Experience, Skills and Qualifications

• Bachelor’s or Master’s degree in Computer Science, Data Science, Mathematics, or a related technical field.
• 5+ years of experience in machine learning engineering or data science, with a focus on machine learning systems, ML platforms, or distributed systems.
• Strong experience building production-grade ML systems, including model deployment and lifecycle management.
• Hands-on experience with MLOps tools and practices, including CI/CD, model monitoring, and experiment tracking.
• Strong programming skills in Python and SQL, or similar languages.
• Experience with cloud platforms and containerization (e.g., AWS/GCP/Azure, Docker, Kubernetes).
• Experience with LLM-based systems in production, including RAG pipelines, embeddings, and vector databases.
• Solid understanding of distributed systems, scalability, and system design trade-offs.
• Proven ability to solve complex technical challenges and deliver scalable solutions.
• Should have. Worked on RAGAS evaluation, precision, recall, writing code, creating product architecture, and RAG pipelines
• Excellent communication and exposure to work with Global Teams.

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