About the Role
We're looking for a Senior AI/ML Engineer who thrives at the intersection of research and production — someone who doesn't just build models, but ships systems that scale. You'll own the full ML lifecycle: architecting robust data pipelines, training and rigorously evaluating models, and deploying them into high-throughput production environments. This is a role for engineers who want their work to move fast, break assumptions (not systems), and directly shape how AI gets built at scale. If you're energized by turning cutting-edge research into real-world impact, this is your next challenge.
Key Responsibilities
– Design, build, and maintain end-to-end ML pipelines — from raw data ingestion to model serving — with a focus on scalability and reliability.
– Develop, train, and rigorously evaluate ML/DL models using sound experimentation practices (A/B testing, offline/online metrics, statistical validation).
– Own the MLOps lifecycle: implement CI/CD pipelines for model training and deployment, version control for datasets and models, and automated retraining workflows.
– Deploy and monitor models in production using containerized, orchestrated infrastructure (Docker, Kubernetes), ensuring low-latency, high-availability inference.
– Collaborate cross-functionally with Data Engineering, Product, and Backend teams to translate business problems into scalable ML solutions.
– Optimize model performance for cost, latency, and accuracy trade-offs across cloud-based training and inference environments.
– Mentor and guide junior engineers and data scientists — conducting code reviews, sharing best practices, and raising the technical bar of the team.
– Stay current with emerging research (LLMs, generative AI, RAG architectures) and proactively identify opportunities to apply them to existing products.
Required Technical Skills
Languages
– Expert-level Python; working knowledge of R or C++ is a plus.
Frameworks
– TensorFlow, PyTorch, Scikit-Learn, Keras.
Data & Cloud
– Strong SQL and NoSQL fundamentals.
– Hands-on experience with at least one major cloud platform (AWS, GCP, or Azure), specifically their ML tooling — SageMaker, Vertex AI, or equivalent.
– Experience with distributed data processing frameworks: Spark, Hadoop.
MLOps & Deployment
– Proficiency with Docker and Kubernetes for containerized deployment.
– Experience building CI/CD pipelines for ML workflows.
– Familiarity with experiment tracking and pipeline orchestration tools like MLflow or Kubeflow.
Advanced / Nice-to-Have
– Practical experience with LLMs, prompt engineering, and Retrieval-Augmented Generation (RAG) architectures.
– Experience fine-tuning transformer-based models for domain-specific use cases.
Qualifications & Experience
– Bachelor's, Master's, or Ph.D. in Computer Science, Data Science, Artificial Intelligence, or a related quantitative field.
– 5+ years of hands-on experience building, training, and deploying production-grade AI/ML models at scale.
– Demonstrated track record of taking models from prototype to production in a real-world business setting.
What We Offer
– Competitive compensation, benchmarked to top-tier tech talent in the industry.
– Comprehensive health insurance coverage for you and your family.
– Annual learning & development stipend for courses, certifications, and conferences.
– A collaborative, high-ownership culture where engineering excellence is celebrated.
– The opportunity to work on cutting-edge AI systems with direct, visible impact.
About Tech Transient
Tech Transient is an AI consulting and digital transformation firm headquartered in Coimbatore, Tamil Nadu.
We partner with enterprises and growth-stage companies to design and deliver intelligent digital products — spanning mobile applications, cloud platforms, and AI-driven solutions. Our mission is to translate emerging technology into measurable business outcomes.
Follow Cutshort