AI/ML Engineer

We are seeking a hands-on AI/ML Engineer to design, build, deploy, and operate enterprise-grade AI/ML and GenAI solutions that improve automation, decisioning, and processing at scale. This role requires building and training fit-for-purpose models in a controlled financial-services environment with limited access to public models and external services, not just configuring third-party tools. The contractor will also mentor existing engineers to strengthen the team’s AI/ML capability.

What You’ll Do

· Own end-to-end delivery of production AI/ML solutions, from problem framing and data analysis through modeling, deployment, monitoring, and retraining.

· Build, train, and validate models (supervised, unsupervised, NLP, retrieval, document intelligence, computer vision, GenAI) using approved data and platforms.

· Establish rigorous evaluation, explainability, bias assessment, and model governance practices suited to a regulated environment.

· Implement MLOps for versioning, CI/CD, deployment, observability, and drift detection.

· Develop secure APIs and integration patterns, partnering with architecture, engineering, data, risk, and cybersecurity teams.

· Take prototypes to scalable production and produce clear documentation, runbooks, and knowledge-transfer materials.

· Mentor developers through design reviews, pairing, and workshops.

· Bachelor’s or advanced degree in Computer Science, Data Science, Statistics, Mathematics, Engineering, or a related field.

· 8+ years in software engineering, data science, or ML, including at least one complete AI/ML lifecycle delivered to production.

· Strong foundation in statistics, probability, linear algebra, optimization, and model evaluation.

· Tech stack will be a combo of onprem tooling like dbs, confluence, sharepoint, and Google Cloud Platform services

· Hands-on model development in Python with standard ML libraries, plus experience with NLP, embeddings, RAG, LLMs, VLMs, or document intelligence.

· Solid grasp of data preparation, feature engineering, class imbalance, leakage prevention, and tuning.

· Experience with MLOps, containerization, model registries, and secure REST APIs and microservices.

· Ability to work within financial-services constraints around privacy, model risk, explainability, and auditability.

· Proven mentoring ability and strong communication with technical and non-technical audiences in Agile, distributed teams.

Preferred

· Cloud-native architecture, Kubernetes, and enterprise CI/CD.

· OCR and Intelligent Document Processing.

· Retrieval and grounding solutions built without relying on pre-packaged frameworks.

· SQL/NoSQL, vector search, and data pipelines.

· Financial services or other regulated-industry background.