Machine Learning Engineer

The Machine Learning Engineer will lead the design, development, and deployment of advanced machine learning models and production-grade AI solutions.
This role works closely with cross-functional teams to integrate ML capabilities into digital applications and product features.
The engineer will mentor junior team members, contribute to strategic planning, and ensure the scalability, performance, and reliability of ML systems in production.
This position requires strong hands‑on expertise across ML frameworks, software engineering, data processing, and emerging AI/LLM technologies.
Key Responsibilities
Lead the development, training, and deployment of advanced machine learning models and algorithms.
Collaborate with teams to build digital applications that integrate ML solutions for end users.
Mentor and guide junior engineers, providing technical direction and best practices.
Work with cross‑functional teams to embed ML capabilities into product features.
Analyze large datasets to identify patterns, insights, and opportunities for model improvement.
Optimize ML models for scalability, high availability, and performance in production.
Stay current with advancements in AI/ML, including GenAI and LLM innovations, and apply them to improve systems.
Conduct code reviews and participate in technical design discussions.
Troubleshoot and resolve issues related to ML models, data pipelines, and deployments.
Communicate complex ML concepts to non‑technical audiences clearly.
Maintain documentation for ML models, processes, and workflows.
Contribute to strategic planning to align ML initiatives with business goals.
Required Qualifications
10+ years of overall software development experience.
5–7 years of hands‑on experience in machine learning model development and deployment.
Proven leadership experience mentoring or guiding junior engineers.
Proficiency in ML frameworks such as TensorFlow, PyTorch, and scikit‑learn.
Strong programming skills in Python, Java, or similar languages.
1–2 years of experience building digital applications using Django or Spring Boot.
Experience with data preprocessing, feature engineering, and building data pipelines.
Proficiency with SQL and NoSQL databases.
Knowledge of AI technologies including GenAI, LLMs, and semantic search.
Understanding of security architecture and data privacy considerations.
Strong problem‑solving skills with a continuous learning mindset.
Effective communication skills for collaborating with technical and non‑technical stakeholders.
Ability to work collaboratively in cross‑functional teams.
Interest in automation, efficiency improvements, and modern engineering practices.
Familiarity with containers and orchestration tools (Docker, Kubernetes).
Preferred Qualifications
Experience applying ML in production environments using modern software engineering patterns.
Experience with emerging AI tools used for model evaluation, optimization, or automation.
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