Senior AI/ML Engineer - LLM & Python, Hyderabad
Listing checked September 12, 2026 · pay as published by Talentxo
Overview
This role centers on building production-grade AI systems, with a strong emphasis on RAG pipelines, LLM-based applications, and document extraction workflows. You will work across the full lifecycle, from designing scalable Python services to deploying models on AWS and Azure using tools like SageMaker, Bedrock, and Azure AI Foundry. The position also involves setting up CI/CD pipelines with GitHub Actions, Azure DevOps, Docker, and Kubernetes, and defining evaluation frameworks that track precision, recall, F1, and field-level accuracy. Collaboration with Product, Data Engineering, and Platform teams is a daily part of translating business needs into working AI solutions. Mentoring teammates and staying current with NLP and LLM advancements round out the role.
What You'll Do10
- 1Lead the design and delivery of production AI/ML solutions, including RAG pipelines, LLM applications, and extraction systems.
- 2Architect scalable AI/ML services in Python with a focus on reliability and production performance.
- 3Build and refine extraction workflows using document parsing, chunking, embeddings, RAG, and LLM-based methods.
- 4Deploy and scale models on AWS and Azure (SageMaker, Bedrock, Azure AI Foundry) and integrate them into data pipelines.
- 5Create and maintain CI/CD pipelines for model deployment using GitHub Actions, Azure DevOps, Docker, and Kubernetes.
- 6Define evaluation frameworks covering precision, recall, F1, and field-level accuracy, and uphold code quality through reviews and testing.
- 7Work with Product, Data Engineering, and Platform teams to turn business requirements into scalable AI solutions.
- 8Mentor team members and share knowledge to raise the team's overall capability.
- 9Research and apply new developments in NLP, LLMs, and extraction techniques.
- 10Follow Agile practices and drive automation across the AI delivery pipeline.
Requirements12
- 13+ years of professional AI/ML engineering experience with a record of delivering production-grade AI systems.
- 2Strong Python and SQL skills, plus hands-on use of ML/data libraries (scikit-learn, pandas, numpy) and deep learning frameworks (PyTorch or TensorFlow).
- 3Demonstrated experience building and deploying production RAG pipelines, document parsing, information extraction, and text processing on large unstructured data.
- 4Proficiency with cloud AI/ML services on AWS and Azure, including SageMaker, Bedrock, and Azure AI Foundry for training, fine-tuning, deployment, and inference at scale.
- 5Hands-on NLP and extraction-focused ML experience with transformers, embeddings, vector databases, RAG, LLM integrations, and agentic workflows.
- 6Experience leading projects or teams, managing technical deliverables, and ensuring high-quality outcomes.
- 7Ability to design and use multi-agentic coding frameworks and orchestration tools such as Claude Code, LangGraph, LangChain, or CrewAI.
- 8Solid grounding in Machine Learning and Deep Learning: supervised/unsupervised learning, CNNs, RNNs, Transformers, NLP, and fine-tuning pre-trained LLMs.
- 9Hands-on MLOps experience with experiment tracking (MLflow, Weights & Biases), model versioning, automated retraining pipelines, and model registry management.
- 10Experience with CI/CD for AI/ML workflows using GitHub Actions, Azure DevOps, or similar; containerization with Docker and orchestration with Kubernetes.
- 11Proficiency in AI observability and monitoring with Prometheus and Grafana, including dashboards, alerts, and SLOs.
- 12Familiarity with data pipeline and orchestration tools such as Apache Kafka or Apache Airflow.
Who Should Apply
Candidates with 3+ years of AI/ML engineering experience and a portfolio of production systems will find this role a strong match. The position suits someone who has built RAG pipelines, worked with LLMs, and deployed models on AWS or Azure using tools like SageMaker, Bedrock, or Azure AI Foundry. Experience with MLOps practices, CI/CD for ML, and monitoring tools like Prometheus and Grafana is also key. This role is less suitable for those without hands-on production experience in NLP, extraction, or cloud-based AI services, or for candidates who have not led projects or mentored others.
Salary Insight
Open to Discussion
Pay and demand for Machine Learning & AI roles
AggregatedTypical pay
$75/hour
This role
Pay not disclosed
Most Machine Learning & AI roles pay $55–$100 per hour.
Based on 621 similar roles that publish pay · 106 publish only a top rate; those count at the rate they gave
Rates shown per hour. Yearly and monthly pay converted; one-time fees and non-USD pay are not included.
- Live similar roles
- 720
- Listed in last 30 days
- 341
- Remote
- 96%
Hiring most right now: micro1 (310) · Mercor (94) · SME Careers (72)
Most requested skills · share of roles
- python17%
- technical writing9%
- llm evaluation7%
- ai evaluation6%
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Compensation
Undisclosed by Talentxo
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Required Skills
Application Tip
Highlight specific projects where you built and deployed RAG pipelines or LLM-based extraction systems, and quantify the impact with metrics like precision, recall, or field-level accuracy. Also mention your experience with AWS SageMaker or Azure AI Foundry for model deployment, as these are core to the role.
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