T
AI/ML Engineer
T
- Posted 14 days ago
- Be among the first 30 applicants
Job Description
About The Role
You will own model lifecycle from feature design through deployment and monitoring.
What You Will Do
You will own model lifecycle from feature design through deployment and monitoring.
What You Will Do
- Build ML pipelines and models.
- Deploy and monitor model behavior in production.
- Collaborate with data and platform teams for robust delivery.
- 3+ years in applied ML with production deployments.
- Strong Python and ML framework proficiency.
- MLOps literacy and statistical grounding.
- LLM and RAG deployment patterns.
- BFSI or healthcare AI domain exposure.
More Info
About Company
Skills:
Python Programming, metrics, evaluation datasets, fine-tuning, LLM and AI fundamentals, Analytical Skills, AI agent architecture, prompt optimization, production ML AI systems, structured testing, benchmarks, model and LLM evaluation, root-cause analysis, model improvement
Skills:
Pytorch, Tensorflow, Python, NLP techniques and libraries, Basic MLOps practices, Azure ML or equivalent cloud ML services
Skills:
data engineering , .NET, production deployment , Java, Scalability, Data Science, Cloud, System Design, Backend Engineering, Integration, Python, ML Engineering, Generative AI, LLMs, Agentic AI, Security, RAG, architecture, AI frameworks, AI application development
Skills:
Aws Lambda, Machine Learning, Design Patterns, Dependency Injection, Rest Apis, Python, OOP principles, event loops, fault-tolerant cloud services, JSON serialization, Distributed System Design, modularity, asyncio, queue-based orchestration
Skills:
Django, Git, Docker, FastAPI, Rest Apis, Python, Microservices, PostgreSQL, Prometheus, Kafka, Grafana, Datadog, Sql, Nosql, Gcp, MySQL, MongoDB, Splunk, Azure, Kubernetes, AWS, Generative AI, LLMs, CI CD automation, AI ML concepts, RAG, Event-driven architectures