{"id":4311297,"url":"https://alion.io/job/kbc-technologies-machine-learning-ml-engineer","title":"Machine Learning (ML) Engineer","company":{"id":3809010,"name":"KBC Technologies","domain":"kbctechnologies.com","url":"https://alion.io/company/kbc-technologies","size_band":null,"is_staffing_agency":false,"employer_type":"services","is_intermediary":false,"listed_via":null,"ats_vendor":null,"truth_index":null},"role":"AI/ML","role_family":"AI/ML","seniority":null,"employment_type":"contractor","work_mode":"hybrid","remote_scope":null,"remote_scope_basis":null,"remote_working_hours":null,"hiring_geo_confidence":"structured","locations":["New York, United States"],"countries":["US"],"hiring_countries":[],"hiring_countries_total":0,"salary":null,"salary_estimate":null,"experience_years_min":null,"visa_sponsorship":false,"relocation_package":false,"has_equity":false,"technologies":[{"name":"CI/CD","optional":false},{"name":"Docker","optional":false},{"name":"Kubernetes","optional":false},{"name":"Machine Learning","optional":false},{"name":"Python","optional":false},{"name":"PyTorch","optional":false},{"name":"Rest API","optional":false},{"name":"TensorFlow","optional":false},{"name":"Feature Store","optional":true}],"status":"live","first_seen_at":"2026-10-16T14:59:27Z","employer_posted_date":null,"last_verified_at":"2026-10-16T14:59:27Z","board_verified":false,"closed_at":null,"days_open":28,"trust":{"level":"not_scored","repost_count":null,"flags":[],"days_open":28},"description":"Job Title: Machine Learning (ML) Engineer\nLocation: New York (Hybrid)\nDuration: W2 Contract\n\nJob Overview\nWe are looking for an experienced Machine Learning (ML) Engineer to design, develop, deploy, and maintain production-grade machine learning solutions. The ideal candidate will have strong Python programming skills, hands-on experience with TensorFlow or PyTorch, and a proven track record of deploying ML models into production environments.\nThe candidate will work closely with Data Science, Data Engineering, and DevOps teams to transform data science prototypes into scalable, reliable, and maintainable ML services and pipelines. Experience with MLOps, containerization, model monitoring, and production ML system design is essential.\nKey Responsibilities\nTranslate data science prototypes into production-ready machine learning services and pipelines.\nDevelop training and inference code with reproducibility, version control, and automated testing.\nBuild and maintain scalable model-serving solutions for online and offline inference.\nOptimize model performance, batching, latency, throughput, and resource utilization.\nImplement and manage ML lifecycle tools, including experiment tracking, model registries, deployment automation, and monitoring.\nCollaborate with Data Engineering teams to develop feature pipelines and establish data contracts.\nImplement model monitoring, drift detection, and performance regression checks.\nEstablish rollback strategies, production health checks, and incident response procedures for ML services.\nApply CI/CD and DevOps practices to automate ML model testing, deployment, and maintenance.\nEnsure ML systems are reliable, scalable, secure, and maintainable in production environments.\nRequired Skills and Qualifications\n5+ years of software engineering experience, including 2+ years deploying and maintaining machine learning models in production.\nStrong programming skills in Python.\nHands-on experience with machine learning frameworks such as TensorFlow and/or PyTorch.\nExperience developing and integrating REST APIs for ML services.\nPractical experience with Docker and Kubernetes for containerization and orchestration.\nStrong understanding of ML system design, including data leakage, training-serving skew, model drift, and performance monitoring.\nExperience implementing MLOps practices, including CI/CD, automated testing, model versioning, and deployment automation.\nUnderstanding of online and batch inference architectures and scalable model-serving patterns.\nPreferred Qualifications\nExperience with feature stores, model registries, and model-monitoring platforms.\nKnowledge of GPU optimization and distributed model training.\nExperience with responsible AI tools, model governance, and compliance requirements.\nFamiliarity with production ML observability, performance optimization, and automated model lifecycle management.\nExperience working collaboratively across Data Science, Data Engineering, and DevOps teams.\nKey Technical Skills\nMust-Have Skills: Python, TensorFlow and/or PyTorch, Docker, REST APIs, Kubernetes, MLOps, Production ML Deployment, CI/CD.\nGood-to-Have Skills: Feature Stores, Model Registry, Model Monitoring, GPU Optimization, Distributed Training, Responsible AI, Model Drift Detection.\nIdeal Candidate Profile\nThe ideal candidate is a hands-on ML Engineer with strong software engineering fundamentals and experience taking machine learning models from experimentation to production. 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