Senior ML Engineer

Tekskills; Hyderabad; On-site; INR 4,000,000 - 7,000,000; Full time

Job summary

Tekskills seeks a Senior Lead Engineer with 5–10 years of AI, automation, and cloud-native experience to lead design, implementation, and mentoring across teams.

You will develop ML models, build RAG pipelines, train and evaluate models, and deploy Python microservices with robust MLOps. Expertise in AWS/Azure/GCP and containers is required.

This role emphasizes collaboration with Data Science, Product, and Engineering, delivering scalable AI solutions for enterprise systems.

Qualifications

• 5–10 years of professional experience in AI/automation.
• Hands-on experience with ML model development, training, and evaluation.
• Proven ability to lead technical teams and mentor junior engineers.
• Strong problem-solving and communication skills.

Responsibilities

• Design and implement scalable ML models tailored to business needs.
• Implement Retrieval-Augmented Generation (RAG) pipelines for enterprise knowledge systems.
• Build robust training pipelines and evaluate models using standard metrics.
• Develop Python-based microservices and integrate with containerized architectures.
• Lead deployment, monitoring, and continuous improvement of ML systems.
• Foster innovation and collaboration across Data Science, Product, and Engineering.

Skills

Python

ML frameworks

MLOps

Cloud platforms

Leadership

Communication

Education

Bachelor's degree in CS/Engineering

Master's degree in related field

Tools

MLflow

Kubeflow

Airflow

Docker

Kubernetes

Job description

Job Summary

We are seeking a highly experienced Senior Lead Engineer with 5 to 10 years of expertise in advanced AI technologies, automation frameworks, and cloud-native solutions. The ideal candidate will be a hands-on technologist with strong leadership skills, capable of designing, implementing, and mentoring teams on cutting-edge AI-driven workflows and microservice architectures.

Responsibilities

• ML Model Development : Design and implement scalable machine learning models tailored to business needs.
• Implement Retrieval-Augmented Generation (RAG) pipelines for enterprise-grade knowledge systems.
• Model Training & Evaluation : Build robust training pipelines, optimize hyperparameters, and evaluate models using industry-standard metrics.
• Build scalable applications using Python and integrate with Microservices.
• Feature Engineering : Develop and maintain efficient feature extraction and transformation processes to improve model accuracy and performance.
• Ensure seamless cloud-based AI deployments across AWS, Azure, or GCP.
• MLOps : Implement CI/CD pipelines for ML workflows, ensuring reproducibility, scalability, and automation of model lifecycle management.
• Collaborate with cross-functional teams to embed AI into existing enterprise systems.
• Model Deployment : Lead deployment of ML models into production environments, ensuring reliability, monitoring, and continuous improvement.
• Foster a culture of innovation, knowledge-sharing, and continuous improvement.
• Collaboration & Leadership : Partner with cross-functional teams (Data Science, Product, Engineering) to deliver impactful solutions.
• Mentor and guide junior engineers in best practices.
• Innovation & Research : Stay updated with the latest ML frameworks, tools, and research to continuously enhance organizational capabilities.

Required Skills & Expertise

• 5-10 years in ML engineering, with proven track record of production-grade ML solutions.
• Strong proficiency in Python, ML frameworks (TensorFlow, PyTorch, Scikit-learn).
• Hands-on experience with MLOps tools (MLflow, Kubeflow, Airflow, Docker, Kubernetes).
• Solid understanding of cloud platforms (AWS, Azure, GCP) for ML deployment.
• ML Model Development, Training, Evaluation
• Feature Engineering & Data Preprocessing
• Model Deployment & Monitoring
• CI/CD for ML pipelines
• Excellent problem-solving, communication, and leadership abilities.

Qualifications

• Bachelors or Master's degree in Computer Science, Engineering, or related field.
• 5 to 10 years of professional experience in AI, automation, and cloud technologies.
• Track record of delivering enterprise-scale AI solutions.
• Experience in leading technical teams and mentoring junior engineers.

Preferred Qualifications

• Certifications in GCP (e.g., Professional Machine Learning Engineer, Data Engineer).
• Experience in Agile/Scrum methodologies.
• Exposure to MLOps and CI/CD pipelines for ML models.
• Experience working in a product-led company.