About
As a
Machine Learning Engineer , you will play a critical role in designing, developing, and deploying advanced machine learning solutions that drive innovation and create measurable business value. You will collaborate closely with business stakeholders, data scientists, and cross-functional engineering teams to transform ideas into scalable, production-ready systems. This role requires both strong technical expertise and leadership ability to guide a small team and deliver impactful solutions.
Key Responsibilities
Lead and deliver machine learning projects from inception through production, building strong relationships with business partners and cross-functional teams. Collaborate with business leaders and subject matter experts to define success criteria and optimize products, features, and models. Partner with data scientists to design, implement, and train machine learning models. Work with infrastructure teams to enhance architecture, scalability, stability, and performance of ML platforms. Design and optimize data pipelines to support high-performance ML model training and inference. Extend and customize machine learning libraries and frameworks for project-specific needs. Define and implement model monitoring, governance, and operationalization processes for ML solutions. Establish objectives and own the technical roadmap for ML platforms, ensuring delivery of results. Define and promote standards of engineering and operational excellence for ML systems. Apply architectural best practices in the delivery of data science and AI solutions. Required Skills & Experience
Strong background in software engineering with proven experience as a Machine Learning Engineer. Bachelor's degree in Computer Science, Computer Engineering, or related field; Master's preferred. Advanced proficiency in
Python, Java, and Scala
with solid CS fundamentals (algorithms, data structures, multithreading). Hands-on experience with
Generative AI , LangChain, and RAG-based techniques. Expertise in ML/DL libraries such as
XGBoost, Scikit-learn, TensorFlow, and PyTorch . Experience building and deploying ML solutions on public clouds ( AWS, GCP ). Familiarity with ML platforms such as
SageMaker, H2O, and DataRobot . Strong knowledge of the ML lifecycle, including containerization, batch vs. real-time inference, and application security. Proven track record in developing and deploying production-grade ML applications with cloud-based automation pipelines. Experience working in
Agile/Scrum
environments with multiple stakeholders. Excellent communication, collaboration, and problem-solving skills with thought leadership and innovative thinking. Nice-to-Have
Experience with
search platforms
(e.g., Solr, Elasticsearch). Hands-on experience building
recommender systems . Exposure to
graph databases
(e.g., Neo4j). Familiarity with
CI/CD tools
(e.g., Jenkins). Domain knowledge in
Financial Services, Insurance, or 401K . AWS Solutions Architect
certification.
Nice-to-have skills
- Java
- PyTorch
- Python
- Scala
- Scikit-learn
- TensorFlow
- XGBoost
Work experience
- Data Engineer
- Fullstack
- Machine Learning
Languages
- English
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