Data / ML Engineer (FDE)

Role: Data / ML Engineer (FDE) | 8–10 years
Location: Remote
Duration: 12+ Months

We are seeking a Data / ML Engineer to turn complex customer problems and data into production‑ready ML solutions with measurable business impact. This role owns the full ML lifecycle—from opportunity identification and data readiness through deployment, monitoring, and continuous improvement—working directly with customer teams in fast‑paced, outcome‑driven engagement

Key Responsibilities

• Identify data and ML opportunities, assess data readiness, and frame problems into actionable ML use cases. 
• Design, build, and deploy end‑to‑end ML pipelines to cloud environments. 
• Run experiments, evaluate models, define metrics, and monitor performance in production.
• Implement feedback loops and continuous learning systems (model refresh, retraining, improvement cycles).
• Ensure strong data quality, observability, experimentation, and metrics thinking across pipelines and models.
• Build and integrate LLM, RAG, and hybrid AI/ML solutions when applicable. 
• Partner with customers to drive adoption and translate results and trade‑offs to technical and business stakeholders. 

Must‑Have Qualifications

• 5–10 years experience in Data Engineering, ML Engineering, Applied ML, or Data Science with end‑to‑end ownership (idea → production → monitoring). 
• Strong Python with PyTorch and/or TensorFlow, scikit‑learn; experience with tree‑based and deep learning models. 
• Solid SQL and large‑scale data processing using Spark / Databricks. 
• Cloud experience on AWS, Azure, or Google Cloud Platform, deploying production ML workloads. 
• Hands‑on MLOps: MLflow, Docker, Git, CI/CD‑style workflows. 
• Proven experience with LLMs, Retrieval‑Augmented Generation (RAG), and hybrid AI/ML architectures. 
• Strong understanding of data governance, Responsible AI, and PII handling. 
• Demonstrated ability to design feedback loops, continuous learning systems, and apply data quality, observability, experimentation, and metrics‑driven thinking in production ML systems.

What Success Looks Like

• ML solutions that move beyond prototypes to stable, monitored production systems
• Clear metrics tied to business outcomes (quality, accuracy, latency, adoption)
• Strong customer trust driven by transparency, explainability, and reliable ML operations