AI Engineer

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

About the Company

testing_1 is a banking-focused company based in Bengaluru, Karnataka, India. The company operates in the banking industry and is building toward a vision expressed as “as das.” Its compact team works in an environment where engineering decisions can remain close to business priorities, allowing people to see the direct effect of what they build. The company values practical execution, clear ownership, and thoughtful problem-solving as it develops technology for its market. Working from Bengaluru, the team brings together technical and business perspectives to create dependable solutions for banking use cases. This is an opportunity to join a focused organization where your contribution can have visible influence on products, systems, and the company’s continued direction.

About the Role

As an AI Engineer, you will own the design, development, and productionization of machine learning and generative AI capabilities for banking-focused solutions. Working in a hybrid model from Bengaluru, you will translate business needs into reliable models, services, and workflows that improve decision quality and operational efficiency. The role spans experimentation, data preparation, model evaluation, deployment, and continuous optimization, with strong emphasis on maintainability and measurable impact. You will work closely with engineering and business stakeholders to manage ambiguous problems, establish practical technical direction, and move solutions from prototype to production. Competitive compensation accompanies the opportunity to shape AI capabilities within a focused team.

Key Responsibilities

• Design and productionize machine learning and generative AI solutions for banking use cases, improving automation, decision support, and the reliability of customer-facing or internal workflows.
• Build robust data and model pipelines that move experiments into maintainable services, reducing deployment friction and creating repeatable paths from development to production.
• Own model evaluation, monitoring, and optimization through meaningful performance measures, ensuring AI systems remain accurate, observable, and aligned with business outcomes over time.
• Partner with engineering and business stakeholders to frame ambiguous problems, select practical approaches, and deliver AI capabilities that solve prioritized operational and customer needs.
• Develop scalable APIs and services that integrate models into existing technology environments, enabling dependable consumption by applications, workflows, and downstream teams.
• Establish responsible engineering practices for data quality, testing, documentation, versioning, and model governance, reducing operational risk as AI adoption expands.
• Investigate emerging AI techniques and tools, converting relevant advances into focused experiments and production improvements rather than isolated technical prototypes.

Essential Skills & Technologies

• Strong Python programming and practical software engineering fundamentals, including object-oriented design, testing, debugging, version control, and writing maintainable production code for AI systems.
• Hands-on experience with machine learning frameworks such as PyTorch, TensorFlow, or scikit-learn, including training, evaluation, feature preparation, and performance optimization.
• Experience designing and deploying generative AI or NLP solutions using large language models, embeddings, prompt patterns, retrieval-augmented generation, or related approaches.
• Proficiency with data processing and storage technologies such as SQL, pandas, NumPy, and structured or semi-structured datasets used in model development and evaluation.
• Practical knowledge of cloud deployment, containers, APIs, and CI/CD practices, with the ability to package, release, and operate AI services reliably.
• Experience with model monitoring, experimentation, observability, and reproducibility, ensuring production behavior can be measured, diagnosed, and improved systematically.
• Ability to communicate technical trade-offs clearly and collaborate with product, engineering, and business stakeholders to connect AI decisions with measurable outcomes.

Additional Plus

• Experience applying AI or machine learning in banking, financial services, risk, fraud, credit, customer operations, or other regulated environments where reliability and traceability matter.
• Familiarity with MLOps platforms, orchestration tools, vector databases, feature stores, or managed cloud AI services that accelerate dependable model delivery.
• Exposure to responsible AI, privacy, security, model governance, or compliance practices that support safe adoption of intelligent systems in business-critical workflows.

What You'll Bring

• Three to five years of experience building and deploying machine learning, generative AI, or intelligent automation systems that have moved beyond experimentation into real-world use.
• A product-minded approach that connects model quality, system reliability, user needs, and business value instead of optimizing technical metrics in isolation.
• Strong ownership in ambiguous environments, with the judgment to define a practical path forward, communicate trade-offs, and deliver incrementally without compromising engineering standards.
• Clear written and verbal communication, enabling effective collaboration across technical and non-technical stakeholders while making complex AI concepts understandable and actionable.