Senior Data Engineer I - QuantumBlack, AI by McKinsey

What you will do:
You will collaborate with business stakeholders, data scientists, and engineering teams to develop, productionize, and scale advanced data, machine learning, and Generative AI solutions that deliver measurable impact for our clients.
You will work across the full analytics and AI lifecycle — from understanding client data landscapes and building robust, reusable data pipelines, to enabling production-grade ML, LLM, and GenAI systems through strong MLOps, LLMOps, and DevOps practices.
Key responsibilities include building and maintaining technical platforms for advanced analytics and AI, designing scalable and reproducible data and ML pipelines, enabling CI/CD for data, ML, and GenAI workflows, and ensuring information security and compliance across cloud environments.
You will collaborate with business stakeholders, data scientists, and internal teams to build and implement domain-focused, reusable data products and analytics platforms. You will design, build, and maintain robust, modular, scalable, and reproducible data pipelines supporting advanced analytics, machine learning, and GenAI use cases. You will understand client data landscapes, assess data quality, and work across structured, semi-structured, and unstructured data. You will map data fields to hypotheses and curate, wrangle, and prepare data for analytics, ML, and GenAI models. You will help build and maintain technical platforms that support end-to-end data and analytics engagements.
You will develop and deploy technology that enables productionization and deployment of ML and Generative AI solutions following industry best practices. You will establish and promote standards for software engineering, MLOps, LLMOps, and DevOps within multidisciplinary delivery teams.
You will design, build, and maintain modern, scalable, and secure CI/CD pipelines that automate development, testing, evaluation, and deployment of data pipelines, ML pipelines, and GenAI applications (including model, prompt, and inference workflows).
You will build and manage cloud-native infrastructure to support ML and GenAI lifecycle management, including experimentation, model tracking, deployment, monitoring, and governance. You will shape and support next-generation technology platforms that enable scaling of ML and GenAI products across teams and clients.
You will contribute to R&D projects and internal assetization efforts (e.g., GEMx / PMPx), building reusable frameworks, libraries, and platforms. You will participate in cross-functional problem-solving sessions with internal teams and clients, from data owners to senior leadership, to address business needs and deliver impactful solutions. You will guide global companies through data, ML, and AI solutions to transform their businesses and enhance performance across industries.
You will be based in Gurugram or Bengaluru as part of a global data engineering community, working in Agile, cross-functional teams alongside project managers, data scientists, machine learning engineers, software engineers, designers, and industry experts.

Who you will work with:
Driving lasting impact and building long-term capabilities with our clients is not easy work. You are the kind of person who thrives in a high performance/high reward culture - doing hard things, picking yourself up when you stumble, and having the resilience to try another way forward.
In return for your drive, determination, and curiosity, we'll provide the resources, mentorship, and opportunities you need to become a stronger leader faster than you ever thought possible. Your colleagues—at all levels—will invest deeply in your development, just as much as they invest in delivering exceptional results for clients. Every day, you'll receive apprenticeship, coaching, and exposure that will accelerate your growth in ways you won’t find anywhere else.
When you join us, you will have:
Continuous learning:
Our learning and apprenticeship culture, backed by structured programs, is all about helping you grow while creating an environment where feedback is clear, actionable, and focused on your development. The real magic happens when you take the input from others to heart and embrace the fast-paced learning experience, owning your journey.
A voice that matters:
From day one, we value your ideas and contributions. You’ll make a tangible impact by offering innovative ideas and practical solutions, all while upholding our unwavering commitment to ethics and integrity. We not only encourage diverse perspectives, but they are critical in driving us toward the best possible outcomes.
Global community:
With colleagues across 65+ countries and over 100 different nationalities, our firm’s diversity fuels creativity and helps us come up with the best solutions for our clients. Plus, you’ll have the opportunity to learn from exceptional colleagues with diverse backgrounds and experiences.
World-class benefits:
On top of a competitive salary (based on your location, experience, and skills), we provide a comprehensive benefits package to enable holistic well-being for you and your family.

Your background:
Bachelor’s degree in Computer Science, Engineering, IT, or a related field
4+ years of relevant experience across data engineering, MLOps, cloud, or DevOps roles
Strong hands-on experience with Python and distributed data processing frameworks (e.g., Spark)
Proven experience designing and maintaining production-grade data pipelines and analytics platforms
Hands-on experience working with data teams to productionize ML and GenAI workflows
Strong expertise in at least one major cloud platform (AWS, Azure, or GCP), including security and service integration
Proven experience building end-to-end CI/CD pipelines for data, ML, and GenAI applications
Solid experience with Docker, Kubernetes, and workflow orchestration tools (e.g., Airflow, Argo, Kedro, Temporal)
Hands-on experience implementing MLOps practices such as experiment tracking, model deployment, and lifecycle management
Practical experience delivering and operating production systems, including APIs, cloud infrastructure, and observability solutions
Working knowledge of SQL and relational databases (PostgreSQL, MySQL, SQL Server, Oracle)
Experience with RAG pipelines, vector databases, embedding models, and LLM frameworks