Machine Learning Engineer

Machine Learning Engineer

A leading technology services firm is seeking an experienced Machine Learning Engineer to architect and implement advanced ML and AI-driven solutions. This role combines hands-on engineering with leadership, requiring deep technical expertise in model development, deployment, and optimization. The ideal candidate will bring a strong command of modern ML frameworks, cloud platforms, and generative AI tools like LangChain and RAG.

This position is hybrid and open to local candidates only .

Position Overview

The Machine Learning Engineer will lead the end-to-end lifecycle of machine learning initiatives—from data pipeline design to production deployment—while collaborating across data science, engineering, and business teams. This is a high-impact role at the intersection of AI innovation, scalable architecture, and applied data science, ideal for engineers passionate about operationalizing ML solutions at scale.

Key Responsibilities

• Lead and execute machine learning projects from concept to production, ensuring performance, scalability, and business impact.

• Collaborate with data scientists to design, implement, and optimize ML and AI models.

• Partner with infrastructure teams to enhance the architecture, stability, and scalability of ML platforms.

• Design and build optimized data pipelines to support real-time and batch inference.

• Extend and maintain existing ML libraries, frameworks, and APIs to accelerate delivery.

• Implement model monitoring, governance, and automation frameworks for robust ML operations (MLOps).

• Drive continuous improvement of ML platform capabilities, staying aligned with the latest technologies and best practices.

• Translate complex ML and AI solutions into actionable business insights and measurable results.

• Provide mentorship and technical direction to junior engineers on the ML team.

Requirements

Required Qualifications

• Bachelor’s degree in Computer Science, Engineering, or a related field ( Master’s preferred ).

• 7+ years of experience in machine learning or applied AI engineering.

• Proficiency in Python, Java, and Scala , with a solid foundation in algorithms, data structures, and multithreading.

• Expertise in ML and DL frameworks such as XGBoost, scikit-learn, TensorFlow, and PyTorch .

• Experience with Generative AI , LangChain , and Retrieval-Augmented Generation (RAG) architectures.

• Hands-on experience with cloud-based ML platforms such as AWS SageMaker, GCP AI Platform, H2O.ai, or DataRobot .

• Strong understanding of model lifecycle management , including containerization, real-time inference, and CI/CD for ML.

• Experience developing and deploying production-grade ML applications on cloud infrastructure.

• Familiarity with Agile/Scrum methodologies and collaborative cross-functional teamwork.

Preferred Experience & Skills

• Strong knowledge of MLOps practices including monitoring, retraining pipelines, and governance frameworks.

• Experience architecting generative AI solutions for enterprise-scale use cases.

• Thought leadership in machine learning innovation, model scalability, and ethical AI practices.

• Excellent communication, collaboration, and problem-solving skills.

Attributes & Mindset

• Analytical thinker with a passion for innovation and applied AI.

• Highly detail-oriented with a focus on code quality and model performance.

• Self-motivated and capable of leading projects in fast-paced environments.

• Committed to continuous learning and staying ahead of emerging ML technologies.

Benefits