Machine Learning Engineer

You establish pipelines and frameworks that allow research models to move seamlessly into production.

You enable RAG-based apps and agentic workflows to be deployed reliably.

You ensure services are scalable, secure, and easy to integrate across teams.

You provide clarity on how different ML models fit different business problems.

Learning from and sharing knowledge and skills with your peers to enhance the team's total impact to the organization.

• Bachelor's or Master's in Computer Science, Artificial Intelligence, Machine Learning, or related field.
• 10+ years of software engineering experience in APIs, cloud deployments, and system integration.
• 3-5 years in ML engineering, with 2+ years in agentic or multi-agent systems.
• Proven must have experience building and deploying RAG pipelines using embedding models and vector search.
• Must have hands-on experience with vector databases such as FAISS, Pinecone, Weaviate, or Milvus.
• Must have experience with agent orchestration frameworks (LangChain, CrewAI, LangGraph, AutoGen etc ).
• Strong background in cloud-native software engineering and microservices architecture.
• Concrete understanding of traditional ML models and their usecses.
• Programming : Advanced Python skills; familiarity with C++, Java, or .NET is a plus.
• Cloud Platforms : Proficiency with AWS services (S3, Lambda, ECS, SageMaker, etc.).
• Databases : Experience with Oracle, Snowflake, vector databases, and knowledge graphs (e.g., Neo4j, RDF/SPARQL).
• DevOps : CI/CD pipelines, Docker, Kubernetes, GitHub Actions.
• AI Ethics : Understanding of Responsible AI principles and ability to identify and mitigate ethical risks.
• Good to have if you have exposure or worked on tools which aid for continuous model evaluation and alerting.
• Stay updated with the latest advancements in Machine Learning world and integrate them into projects.
• Communicate complex technical concepts to non-technical stakeholders.

Integration expertise: You can take research outputs and turn them into production-ready APIs and applications.

ML/GenAI awareness: You understand which types of models are used for which problems, and can connect them effectively to real-world data.

System design: You know how to containerize, scale, and monitor services.

Engineering discipline: You bring CI/CD, versioning, and logging best practices to ML/GenAI deployment.

Experience building observability systems for agent performance tracking (e.g., Prometheus, Grafana, OpenTelemetry).

Strong grasp of AI safety, fairness, and governance.

Ability to research, evaluate, and implement emerging tools and frameworks.

Demonstrated success in proof-of-concept development, experimentation, optimization, and production deployment.

Proficiency in designing scalable, distributed systems and cloud-native applications.

Collaborative mindset with strong communication and problem-solving skills.

Equal Opportunity Employer: Race, Color, Religion, Sex, Sexual Orientation, Gender Identity, National Origin, Age, Genetic Information, Disability, Protected Veteran Status, or any other legally protected group status.

At Randstad Digital, we welcome people of all abilities and want to ensure that our hiring and interview process meets the needs of all applicants. If you require a reasonable accommodation to make your application or interview experience a great one, please contact

Pay offered to a successful candidate will be based on several factors including the candidate's education, work experience, work location, specific job duties, certifications, etc. In addition, Randstad Digital offers a comprehensive benefits package, including: medical, prescription, dental, vision, AD&D, and life insurance offerings, short-term disability, and a 401K plan (all benefits are based on eligibility).

This posting is open for thirty (30) days.