AI/ML Engineer

Location: Bengaluru, Karnataka, India | Experience: Mid Level (3-5 years)

About the Company

testing_2 is a Bangalore-based company operating in the banking industry, with a focused team of 1-10 employees. Our work is grounded in using technology to support better outcomes for organizations operating in a highly regulated and data-rich environment. As part of a growing business, team members have meaningful ownership and direct influence on how products, systems, and customer outcomes develop. The company is building its presence through a practical, technology-led approach and values people who can turn complex problems into usable solutions. You will work closely with a small, collaborative team where decisions can move quickly and contributions are visible. The role offers the opportunity to apply artificial intelligence and machine learning to real business needs while helping establish strong engineering foundations. We are looking for thoughtful builders who combine technical depth with commercial awareness, communicate clearly, and take responsibility for delivering dependable results. This is an opportunity to contribute to an ambitious technology environment while shaping solutions that can create measurable value in banking.

About the Role

As an AI/ML Engineer, you will own the design, development, and improvement of machine learning systems that address meaningful banking and business problems. You will translate ambiguous requirements into reliable data products, models, and services that can be measured in production. The role combines applied machine learning, software engineering, experimentation, and deployment, with accountability for model quality, system performance, and business impact. You will collaborate with product, engineering, and business stakeholders to identify high-value opportunities, define success metrics, and deliver solutions that users can trust. Success means moving from data and hypotheses to maintainable production capabilities, while improving accuracy, efficiency, risk management, or customer outcomes. You will also strengthen engineering practices around reproducibility, monitoring, documentation, and responsible use of data.

Key Responsibilities

• Own the full machine learning lifecycle, from problem framing and data preparation through model development, deployment, monitoring, and continuous improvement, ensuring measurable business outcomes.
• Build production-ready ML services and pipelines that integrate reliably with existing engineering systems, improving decision quality, operational efficiency, or customer experience.
• Partner with product, engineering, and banking stakeholders to define clear success metrics, prioritize high-value use cases, and convert business needs into scalable technical solutions.
• Design experiments and evaluation frameworks that establish model performance, identify trade-offs, and guide investment toward approaches that deliver reliable production impact.
• Implement practices for data quality, feature management, reproducibility, model monitoring, and documentation so systems remain dependable, explainable, and maintainable over time.
• Investigate model and system failures, identify root causes, and lead improvements that reduce operational risk while protecting the quality of customer-facing and internal outcomes.
• Communicate technical findings clearly to technical and non-technical audiences, influencing decisions through evidence, practical recommendations, and transparent articulation of limitations.

Essential Skills & Technologies

• Strong Python programming and practical experience with machine learning libraries such as scikit-learn, TensorFlow, or PyTorch, applying sound software engineering standards to production systems.
• Hands-on expertise in supervised and unsupervised learning, feature engineering, model evaluation, experimentation, and statistical reasoning, with the ability to select methods based on business constraints.
• Experience working with SQL, data processing workflows, and structured or unstructured datasets, including the ability to assess data quality and build dependable training inputs.
• Understanding of MLOps practices, including model serving, CI/CD, monitoring, versioning, reproducibility, and deployment through cloud or containerized environments.
• Ability to design scalable APIs, services, and pipelines, collaborating effectively with software engineers to integrate models into secure, reliable, and maintainable products.
• Strong communication and problem-solving skills, with the judgment to explain model behavior, risks, assumptions, and expected business impact to varied stakeholders.

Additional Plus

• Experience applying machine learning in banking, fintech, fraud detection, credit, risk, compliance, or other regulated and data-sensitive environments, with awareness of responsible AI requirements.
• Familiarity with cloud platforms, Docker, Kubernetes, workflow orchestration, distributed processing, or feature stores that support reliable machine learning operations at scale.
• Exposure to large language models, natural language processing, recommendation systems, computer vision, or generative AI, with practical understanding of evaluation and production limitations.

What You'll Bring

• You bring strong ownership and a delivery mindset, taking responsibility for turning unclear business questions into practical machine learning systems that improve measurable outcomes rather than stopping at prototypes.
• You bring technical depth across modelling and engineering, balancing experimentation with reliability, maintainability, security, and performance so solutions create lasting value in production.
• You bring structured thinking and curiosity, using data, experiments, and clear success measures to make decisions while remaining open to feedback and changing assumptions.
• You bring effective collaboration, communicating complex ideas in simple language and building trust with product, engineering, and business partners across a small, fast-moving team.

Why Join Us

You will join a small team where your work has visible impact and where ownership is close to the people making decisions. In this environment, an AI/ML Engineer can shape not only individual models, but also the engineering practices, data foundations, and product capabilities that determine whether machine learning delivers value in the real world. The banking industry creates meaningful technical challenges around trust, accuracy, risk, customer experience, and responsible use of data. You will have the opportunity to solve those challenges with a team that values practical execution, clear thinking, and measurable outcomes. The role offers broad exposure across the machine learning lifecycle, from identifying valuable use cases to operating dependable production systems. You will collaborate directly with stakeholders, see how your decisions affect business performance, and help build capabilities that can scale with the organization. For an engineer who enjoys autonomy, learning, and meaningful technical responsibility, this is a chance to make a substantial contribution while growing with an ambitious technology-led company.

What We Offer

• Meaningful ownership in a small, collaborative team where your technical decisions are visible and connected directly to product and business outcomes.
• Competitive compensation and the opportunity to work on practical AI/ML challenges within the banking industry, including trust, risk, efficiency, and customer value.
• A hybrid working environment that supports focused execution, direct collaboration, continuous learning, and close partnership with engineering, product, and business stakeholders.