JOB ID: QT-SNB-08-349

Role Overview

We are looking for an experienced ML Engineer with strong expertise in Python, Machine Learning, Generative AI, Agentic AI, and Microsoft Azure .

The ideal candidate will have hands-on experience in building and deploying machine learning models, developing scalable ML pipelines, creating intelligent AI agents, and orchestrating agentic workflows using Azure Data and AI platforms.

The candidate will work closely with ML engineers, data engineers, backend developers, frontend teams, architects, and researchers to build secure, scalable, reliable, and production-ready AI solutions .

Key Responsibilities Machine Learning Engineering

- Develop, maintain, and optimize scalable machine learning pipelines .
- Build, train, evaluate, and deploy ML models using Python, TensorFlow, PyTorch , or equivalent frameworks.
- Integrate machine learning workflows with Azure ML .
- Implement data preprocessing, feature engineering, model training, validation, and deployment workflows.
- Optimize model performance, scalability, latency, and resource utilization.
- Support productionization and lifecycle management of machine learning models.

Generative AI & LLM Engineering
- Design and develop Generative AI and LLM-based applications .
- Experiment with and integrate foundation models and LLMs into enterprise applications.
- Develop solutions involving prompt engineering, RAG, embeddings, context management, and LLM orchestration .
- Evaluate LLM responses for accuracy, relevance, reliability, safety, and consistency.
- Implement model and prompt optimization strategies.
- Work with modern AI frameworks and Azure AI services.

Agentic AI
- Design and implement AI agents and agentic AI workflows .
- Develop intelligent agents capable of planning, reasoning, decision-making, and tool utilization.
- Implement agent orchestration and multi-step workflows using LLMs.
- Design production-ready architectures for stateful and stateless AI agents.
- Integrate AI agents with APIs, enterprise systems, tools, databases, and external services.
- Develop mechanisms for agent memory, context handling, tool calling, and workflow execution.
- Improve agent reliability, adaptability, and decision-making through continuous evaluation.

AI Safety, Evaluation & Guardrails
- Implement AI safety mechanisms and guardrails for production AI systems.
- Design validation and control mechanisms to reduce unsafe, inaccurate, or unwanted outputs.
- Establish evaluation frameworks and metrics for AI agents and LLM applications.
- Monitor model and agent behavior in production.
- Analyze user interactions and feedback to identify areas for improvement.




- Develop feedback loops to continuously improve AI system performance and reliability.

Microsoft Azure & AI Platforms
- Work extensively with Microsoft Azure and its AI/Data ecosystem.
- Develop and deploy solutions using:
- Azure ML
- Azure Databricks
- Microsoft Fabric
- Microsoft AI Foundry

- Design scalable cloud architectures for ML and Generative AI workloads.
- Integrate Azure AI and data services into end-to-end AI solutions.
- Support cloud-based model serving, deployment, monitoring, and optimization.

ML Pipelines & Productionization
- Design and implement scalable ML/AI pipelines .
- Automate model training, validation, deployment, and monitoring.
- Implement appropriate CI/CD and MLOps practices for AI workloads.
- Support model serving and production deployments.
- Monitor model performance, drift, latency, availability, and resource utilization.
- Troubleshoot production issues and perform root-cause analysis.
- Ensure AI systems are scalable, maintainable, secure, and reliable.

Research & Optimization
- Conduct experiments and research on emerging Generative AI, LLM, and Agentic AI technologies.
- Evaluate new models, frameworks, tools, and architectures.
- Perform performance benchmarking and comparative evaluations.
- Explore reinforcement learning and feedback-driven approaches for agent improvement.
- Rapidly prototype and iterate on AI solutions in a fast-paced environment.

Cross-Functional Collaboration
- Collaborate with backend, data engineering, ML platform, frontend, DevOps, and architecture teams.
- Work with senior engineers, architects, researchers, and product stakeholders.
- Translate business requirements into technical AI/ML solutions.
- Participate in architecture reviews and technical design discussions.
- Document AI architectures, workflows, experiments, and implementation approaches.
- Mentor junior engineers and contribute to engineering best practices.

Required Qualifications
- 6-8 years of experience in Machine Learning / AI Engineering.
- Strong hands-on programming experience with Python .
- Hands-on experience with TensorFlow, PyTorch , or equivalent ML frameworks.
- Proven experience developing, training, deploying, and monitoring machine learning models.




- Strong hands-on experience with Generative AI, LLMs, and Agentic AI .
- Strong experience with Microsoft Azure and Azure AI/Data platforms.
- Hands-on experience with:
- Azure ML
- Azure Databricks
- Microsoft Fabric
- Microsoft AI Foundry

- Experience developing scalable ML/AI pipelines .
- Experience with model deployment, serving, monitoring, and productionization.
- Strong understanding of AI agent orchestration, tool calling, planning, reasoning, evaluation, safety, and guardrails .
- Strong analytical and problem-solving skills.
- Ability to work effectively in a fast-paced and team-oriented environment.

Good to Have
- Experience with Reinforcement Learning and agent feedback mechanisms.
- Experience building AI solutions for enterprise or security applications .
- Strong knowledge of RAG architectures .
- Experience with vector databases, embeddings, and semantic search.
- Strong knowledge of Prompt Engineering .
- Experience with LLM evaluation frameworks and methodologies.
- Experience with model optimization and fine-tuning.
- Knowledge of AI safety and responsible AI practices.
- Experience integrating AI solutions with backend services, REST APIs, databases, and frontend applications .
- Experience with MLOps, MLflow, CI/CD, Docker, and Kubernetes.
- Experience with multi-agent architectures and agentic workflow frameworks.

Key Skills Must Have

Python | Machine Learning | Azure ML | Generative AI | LLM | Agentic AI | TensorFlow / PyTorch | Azure Databricks | Microsoft Fabric | Microsoft AI Foundry | ML Pipelines | Model Deployment | AI Agent Deployment | AI Evaluation | AI Guardrails

Good to Have

Reinforcement Learning | RAG | Prompt Engineering | Vector Databases | Embeddings | LLM Evaluation | Model Optimization | Fine-Tuning | MLOps | MLflow | Docker | Kubernetes | REST APIs

Ideal Candidate Profile

The ideal candidate is a hands-on ML/AI Engineer with strong experience across Machine Learning, Generative AI, LLMs, Agentic AI, and Azure AI/Data platforms .

The candidate should be capable of taking AI solutions from experimentation and prototyping through model development, agent orchestration, deployment, evaluation, monitoring, and productionization .

Strong practical experience with Python, Azure ML, Azure Databricks, Microsoft Fabric, Microsoft AI Foundry, TensorFlow/PyTorch, ML pipelines, LLMs, and AI agents is essential.

Disclaimer: This job posting has been aggregated from external source. Role details, content, and availability are subject to change. Applicants are advised to confirm the latest information directly on the company website before applying.

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