Location: Noida, IndiaThales people architect identity management and data protection solutions at the heart of digital security. Business and governments rely on us to bring trust to the billons of digital interactions they have with people. Our technologies and services help banks exchange funds, people cross borders, energy become smarter and much more. More than 30,000 organizations already rely on us to verify the identities of people and things, grant access to digital services, analyze vast quantities of information and encrypt data to make the connected world more secure.Present in India since 1953, Thales is headquartered in Noida, Uttar Pradesh, and has operational offices and sites spread across Bengaluru, Delhi, Gurugram, Hyderabad, Mumbai, Pune among others. Over 1800 employees are working with Thales and its joint ventures in India. Since the beginning, Thales has been playing an essential role in India’s growth story by sharing its technologies and expertise in Defence, Transport, Aerospace and Digital Identity and Security markets.
Position Summary
Applied AI/ML Engineer design, develop, and industrialize AI/ML solutions that improve customer engagement and operational efficiency across MCS. The role spans machine learning, data pipeline management, model deployment, generative and agentic AI solution development. Taking ideas from PoC/MVP to production in close partnership within MCS, with CDI CTO organization and relevant Thales entities. You will operate within a small, multidisciplinary engineering team, evaluating emerging technologies, maintaining a Tech Radar of risks/opportunities, and deliver evidence-driven solutions under real production platform constraints.
Essential Functions / Key Areas Of Responsibility
- Collaborate closely with stakeholders across product teams, marketing teams and engineering teams to define AI/ML use cases, translate business problems into AI/ML-powered solutions.
- Conduct Data Exploration and analysis to extract actionable insights from structure and unstructured data.
- Apply state-of-the-art ML/DL methods (supervised/unsupervised learning, deep networks) across diverse use cases (e.g., anomaly detection, predictive analysis, fraud detection).
- Implement end-to-end ML pipelines (data ingestion, preprocessing, training, evaluation, deployment, monitoring).
- Integrate ML models into production systems on-premises or Cloud platforms (e.g. AWS, GCP, …).
- Implement monitoring and logging to track model drift and performance using appropriate metrics.
- Build PoC/MVP using open/closed‑source generative AI, agentic AI frameworks to assess feasibility and potential impact, helping to de-risk the investment before committing to full-scale development.
- Fine-tune and adapt pre-trained models to business use case specific needs.
- Design and develop robust end-to-end AI solution, including backend services, scalable APIs, and integration with user-facing front-end, applying software engineering best practices (CI/CD, version control, automated testing,…)
- Document solution architecture, system design, integration flows, operation and best practices for reproducibility and knowledge sharing.
- Stay current with AI/ML research and industry trends to propose new techniques and tools.
Minimum Requirements: Skills, Experience & Education
- Master’s or bachelor’s degree in computer science, Computer Engineering, AI/Data disciplines or related field.
- Senior Profile with min 5 years of experience in delivering applied AI or ML solution, from PoC to solution industrialization.
- Proficiency in Python. Familiarity with other programming and scripting languages is a plus.
- Hands‑on with Data Science tools/practices (data prep, validation, evaluation) and open‑source ML/DL libraries.
- Familiarity with Generative AI open/close source, framework and workflows (fine‑tuning and/or RAG) or strong motivation to learn and apply quickly.
- Tooling: Git, Linux/UNIX & shell scripting, Docker / Docker Compose; basic cloud exposure (e.g., AWS).
- Experience with PyTorch, TensorFlow and knowledge of Spark/Hadoop or distributed data processing is a plus.
Preferred Qualifications
- Independent and analytical thinker, with proactive approach to problem solving.
- Able to work independently as well as working with cross-functional teams.
- Adept at switching between diverse technologies and tasks to meet evolving project needs.
Physical Demands:
- Highly autonomous
- Comfortable dealing with fast paced technology evolution and project uncertainties.
Special Position Requirements:
- Global mindset to foster strong working relationships with colleagues worldwide. Flexible and willing to adapt working hours on occasion when necessary.
At Thales we provide CAREERS and not only jobs. With Thales employing 80,000 employees in 68 countries our mobility policy enables thousands of employees each year to develop their careers at home and abroad, in their existing areas of expertise or by branching out into new fields. Together we believe that embracing flexibility is a smarter way of working. Great journeys start here, apply now!
