Our client is seeking a Machine Learning Engineer. Due to the nature of the work, client is unable to hire H1-B candidates at this time.

Work will be done remotely.

Machine Learning Engineer

As a ML Engineer for this key client engagement:

We are seeking a talented and experienced Machine Learning Engineer to join our dynamic, multi-functional team. In this role, you will be instrumental in building and scaling AI-enabled intelligent products that deliver insights across diverse and highly regulated sectors including healthcare, legal, construction, and real estate. If you are passionate about transforming complex data into real-world impact and thrive in a collaborative, fast-paced environment, we encourage you to apply.

On any given day, we hope that you will:

As a Machine Learning Engineer, you will play a pivotal role in the end-to-end lifecycle of our AI products, focusing on scalability, reliability, and performance. Your responsibilities will include:

• Building and Scaling Predictive Models: Design, develop, and scale real-time predictive models that power critical insights across various This can include tackling challenges such as state-of-the-art document intelligence, patient risk stratication in healthcare, in-depth contract analysis for legal applications, construction site anomaly detection, and advanced property valuation in real estate.
• Data Pipeline & Feature Store Development: Design and build robust, automated data pipelines to ingest, transform, and analyze data at scale. You will also develop and maintain feature stores to fuel multi-tenant ML systems across highly regulated and complex domains, ensuring data quality and
• Model Productionization: Collaborate closely with peers to productionize the use of models and workows, possibly even custom This could involve parsing legal documents, assessing wildre risk via satellite imagery, or detecting anomalies on construction sites, ensuring these models are robust, secure, and performant in production environments.
• Leveraging and Deploying Existing Models: Evaluate, integrate, and deploy existing pre-trained models (e.g., from OpenAI, Gemini) and make informed trade-off decisions based on performance, cost, and ethical You will also work to secure these integrations within enterprise environments, utilizing platforms like Azure AI Foundry for responsible and compliant AI solutions.
• Infrastructure Leadership: Lead the charge on infrastructure improvements for model training, versioning, and deployment. You will leverage technologies like Kubernetes and MLFlow to build scalable and reliable AI product delivery
• Rapid Prototyping & Productionization: Rapidly prototype new machine learning capabilities and features, then diligently promote those prototypes into production-ready code, ensuring high quality through comprehensive automated testing and adherence to best practices.

What You'll Get To Work With:

You'll have the opportunity to work with a cutting-edge technology stack, including:

• Machine Learning Frameworks: LLMs, PyTorch, TensorFlow, MLFlow
• Big Data Technologies: Databricks, Spark
• Container Orchestration: Kubernetes, Docker
• Data Streaming: Kafka, Flink
• APIs & Development: REST APIs, CI/CD
• Data Storage: SQL/NoSQL databases, object storage, le systems, JSON

In order to succeed, you will need to have some combination of the following:

• Experience: 3-5+ years of relevant industry experience in machine learning engineering or a related eld. Experience consulting directly with clients is a plus
• Programming: Solid programming fundamentals, including an understanding of algorithms, functional programming, data structures, and API design, particularly in Python. Experience with other languages like JavaScript, Scala, or C++ is also valued.
• Software Engineering Best Practices: Procient in modern software engineering best practices, including git, automated testing, and Continuous Integration/Continuous Deployment (CI/CD)
• Working in Existing Code: As consultants we often work in code we re not familiar You ll be expected to adapt to these, leaving them better than you found them, while not losing track of the core objectives.
• Data Storage: Comfortable with various data storage technologies, including SQL and NoSQL databases, object storage, le systems, streaming data solutions, and JSON data formats.
• Creative Problem-solving: When encountering blockers or data inconsistencies, you're someone who proactively looks for alternative suggestions, exploring outside the initial requirements to propose solutions instead of just raising ags.
• Data Processing: Hands-on experience with big data processing technologies such as Spark, Hadoop, Flink, and Kafka
• Operations & Deployment Knowledge: Familiarity with operations and deployment concepts, including Infrastructure as Code, Docker, and Kubernetes
• Software Design & Architecture: Strong understanding of software design principles and architectural patterns for building scalable and maintainable systems
• Ecosystems: An understanding of different vendor ecosystems, including considerations for open-source off-the-shelf solutions, build vs. buy decisions, and integration platforms.