Machine Learning Engineer
Posted Updated 13
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Summary
Senior Machine Learning Software Engineer building production-grade ML infrastructure at Blackbuck Insights: architecting training, evaluation, deployment and monitoring pipelines (MLOps, CI/CD, model serving) using Python plus systems languages, Docker/Kubernetes, AWS SageMaker/Vertex AI, and frameworks like MLflow and TFX, while mentoring junior engineers.
Job Description: Senior Full-Stack AI/ML Engineer
Experience: 4+ Years in AI/ML
Position: Senior Full-Stack AI/ML Engineer
Primary Skills: Agentic AI, LLMOps, Production Chatbots, Traditional Machine Learning, Recommendation Systems, Python, FastAPI, SQL
Job Summary
We are looking for a Senior Full-Stack AI/ML Engineer with 4+ years of experience in Artificial Intelligence and Machine Learning. The candidate must have hands-on experience in two distinct areas: Agentic AI and production-grade chatbot development, and traditional Machine Learning-based recommendation systems.
The ideal candidate should be capable of developing stateful multi-agent workflows, building production-ready Retrieval-Augmented Generation (RAG) pipelines, implementing LLMOps for tracing and evaluating AI applications, and developing predictive recommendation engines using traditional ML algorithms.
Strong programming skills in Python, FastAPI, and SQL, along with experience working with PostgreSQL and Redis, are required.
Experience: 4+ Years in AI/ML
Position: Senior Full-Stack AI/ML Engineer
Primary Skills: Agentic AI, LLMOps, Production Chatbots, Traditional Machine Learning, Recommendation Systems, Python, FastAPI, SQL
Job Summary
We are looking for a Senior Full-Stack AI/ML Engineer with 4+ years of experience in Artificial Intelligence and Machine Learning. The candidate must have hands-on experience in two distinct areas: Agentic AI and production-grade chatbot development, and traditional Machine Learning-based recommendation systems.
The ideal candidate should be capable of developing stateful multi-agent workflows, building production-ready Retrieval-Augmented Generation (RAG) pipelines, implementing LLMOps for tracing and evaluating AI applications, and developing predictive recommendation engines using traditional ML algorithms.
Strong programming skills in Python, FastAPI, and SQL, along with experience working with PostgreSQL and Redis, are required.
Requirements
Experience and Qualifications
4+ years of professional experience in AI/ML.
Proven experience developing and deploying production-grade chatbots.
Hands-on experience building multi-agent workflows and RAG applications.
Experience implementing LLM tracing, observability, and evaluation.
Demonstrated experience developing, training, and deploying traditional ML-based recommendation systems.
Strong Python, FastAPI, and SQL skills.
Experience with PostgreSQL and Redis.
Excellent analytical, troubleshooting, and problem-solving skills.
4+ years of professional experience in AI/ML.
Proven experience developing and deploying production-grade chatbots.
Hands-on experience building multi-agent workflows and RAG applications.
Experience implementing LLM tracing, observability, and evaluation.
Demonstrated experience developing, training, and deploying traditional ML-based recommendation systems.
Strong Python, FastAPI, and SQL skills.
Experience with PostgreSQL and Redis.
Excellent analytical, troubleshooting, and problem-solving skills.