AI/ ML Engineer

Software Engineer

We are looking for an AI/ML Engineer to build, productionize, and optimize ML and Generative AI solutions that power intelligent, question-driven analytics and workflow automation. You will design robust data/feature pipelines, implement LLM- and ML-based services (including RAG and agentic patterns), and ship secure, explainable, and observable models into production—working closely with product, data, platform, and QA teams in an Agile environment.

Key Responsibilities

• Model Development & Generative AI
• Design, train, fine-tune, and evaluate ML and LLM models for use cases such as intent classification, retrieval-augmented generation (RAG), forecasting, recommendations, and anomaly detection.
• Engineer prompts, system messages, and guardrails, and implement fallback strategies (e.g., safe completions, rules-based checks, defaults) to ensure reliability and usefulness.
• Build agentic workflows that plan, call tools/APIs, reason over structured/unstructured data, and return explainable outputs.
• Data, Features & Evaluation
• Build reliable data/feature pipelines (batch & near-real-time) and maintain feature stores; ensure data quality, lineage, and reproducibility.
• Establish offline/online evaluation: A/B tests, quality gates, bias/fairness checks, hallucination detection, and domain-specific accuracy metrics.
• Implement semantic/metadata alignment (business glossary, metric catalog, synonyms) so models interpret business questions consistently.
• MLOps & Platform Engineering
• Own end-to-end model lifecycle: packaging, versioning, deployment, canary/A-B rollout, drift detection, retraining, rollback, and cost/latency optimization.
• Instrument observability (tracing, logging, metrics, LLM/ML telemetry) to monitor performance, safety, and usage; build dashboards and alerts.
• Integrate with CI/CD pipelines (tests, security scans, infra-as-code), ensuring repeatable and compliant releases.
• Security, Compliance & RBAC
• Embed PII protection, RBAC inheritance, sample-size enforcement, peer-group rules, and audit trails in data/model services.
• Contribute to risk assessments and responsible AI practices (explainability, human-in-the-loop, model cards, usage policies).
• Collaboration & Delivery