Sr. AI / ML Engineer

CompuForce is continuously building a strong pipeline of Senior AI/ML Engineers for our enterprise clients across finance, healthcare, retail, and technology. This posting represents the type of candidates we regularly place for mission-critical modernization, AI integration, and data engineering initiatives.

We welcome applications from experienced AI/ML engineering talent interested in being considered for future roles, consulting engagements, and full-time placements.

Role Overview

The Senior AI/ML Engineer will design, build, and operationalize scalable machine learning and AI systems within cloud-native data platforms. This role is ideal for candidates with deep experience in LLMs, NLP, distributed data processing, MLOps, and cloud modernization.

You will collaborate with Data Engineering, Product, Risk/Compliance, and Cloud teams to deliver production-grade solutions for high-impact analytical and predictive workloads.

Key Responsibilities

• Design and implement end-to-end ML pipelines, including feature engineering, model training, validation, deployment, and monitoring.
• Build and optimize scalable ETL/ELT pipelines using Python, Spark/PySpark, SQL, and modern lakehouse architectures.
• Develop and fine-tune Large Language Models (LLMs) for summarization, Q&A, intelligent search, and domain-specific text analytics.
• Build NLP models for structured and unstructured data extraction using Transformers, Hugging Face, LangChain, and related frameworks.
• Implement MLOps practices using MLflow, GitHub, Jenkins, Docker, Kubernetes, and cloud ML services.
• Collaborate with Data Engineering teams to ensure data quality, lineage, governance, and compliance across the AI lifecycle.
• Apply model explainability (LIME/SHAP) for regulated industries like finance and healthcare.
• Support production operations through monitoring, drift detection, retraining, and performance tuning.

Required Qualifications

• 7+ years of combined experience in AI/ML engineering, data engineering, or advanced analytics.
• Strong proficiency in Python, SQL, Spark/PySpark, and distributed processing frameworks.
• Hands-on experience with LLMs, NLP, and transformer-based architectures.
• Experience deploying models in cloud ecosystems such as Azure, AWS, or hybrid cloud architectures.
• Demonstrated MLOps experience, including CI/CD, model versioning, model registry, and containerized deployments.
• Expertise in data modeling (Star/Snowflake schemas) and data warehouse/lakehouse optimization.
• Familiarity with regulated environments (e.g., HIPAA, PII, financial regulatory requirements) is a strong advantage.
• Strong communication skills and ability to partner with cross-functional stakeholders.

Preferred Experience

(Not mandatory; enhances matching opportunities)

• Experience with Delta Lake, Databricks, and Kafka.
• Exposure to generative AI, RAG pipelines, and enterprise search systems.
• Prior work in financial services, healthcare systems, or large enterprise platforms.
• Experience supporting risk modeling, patient analytics, or retail personalization systems.