AI / ML Engineer

We are looking for an experienced AI / ML Engineer to design, build and productionize AI-driven decision-support solutions. In this hands-on role, you will collaborate with business teams, Data Scientists, and Data Engineers to translate business problems into analytical models, ML algorithms, and GenAI capabilities, taking them from experimentation through validation and deployment.

Key responsibilities include:

• Translate business problems into appropriate AI/ML problem statements, features, algorithms, and evaluation frameworks.
• Perform exploratory analysis and feature engineering on large enterprise datasets.
• Design, train, tune, back-test, and validate machine-learning and statistical models.
• Build models for use cases such as price and cost prediction, demand forecasting, supplier segmentation, anomaly detection, clustering, risk prediction, and recommendation engines.
• Develop reusable model components and decision logic applicable across multiple business use cases.
• Where appropriate, develop LLM/GenAI solutions for unstructured information like contracts and specifications.
• Build RAG pipelines, embeddings, semantic search, and vector-based retrieval where relevant.
• Evaluate model performance using statistical and business metrics, establishing confidence thresholds and explainability.
• Work with business SMEs to validate model outputs for practical and commercial relevance.
• Package models for deployment through APIs/model-serving frameworks and collaborate with developers for integration into user-facing applications.
• Establish MLOps practices covering experiment tracking, model versioning, deployment, monitoring, and retraining.
• Monitor model accuracy, drift, and performance post-deployment, establishing feedback loops with actual business outcomes.

Expected technical skills include:

• Core AI/ML: Proficiency in Python, Pandas, NumPy, scikit-learn, with a strong understanding of supervised/unsupervised ML, time-series modeling, clustering, and statistical techniques.
• Advanced modeling: Exposure to XGBoost/LightGBM, optimization techniques, and recommendation systems. Experience with PyTorch/TensorFlow is advantageous.
• GenAI: Hands-on exposure to LLMs, prompt engineering, embeddings, RAG, and vector databases. Familiarity with LangChain/LlamaIndex or equivalent frameworks is a plus.
• MLOps: Experience with MLflow/model registries, Git, Docker, APIs, and CI/CD for productionizing ML models.
• Cloud: Experience deploying AI/ML workloads on AWS, with familiarity in S3, EC2, SageMaker, and Bedrock being advantageous.
• Data: Strong SQL skills and the ability to work with large, imperfect enterprise datasets.

The ideal candidate will have 4–5 years of hands-on experience in Data Science, Machine Learning, or AI Engineering, having independently taken multiple models from business problem to deployment. You should have strong fundamentals in ML, be able to explain complex model outputs to non-technical stakeholders, and possess a strong experimentation and problem-solving mindset. We seek a dynamic, entrepreneurial individual comfortable in rapidly evolving environments, able to work closely with business SMEs, Data Engineers, and application developers to convert models into usable decision-support products.

What we offer: a collaborative work environment, opportunities for professional growth, and the chance to work on innovative AI/ML projects.