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.
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