Senior Associate
Job Description
- Own LLM features end to end: prompts, retrieval, tool use, and APIs
- Build and tune models with classical ML, deep learning, and neural networks
- Ability to use Hugging Face models and datasets for fine-tuning, inference, and evaluation
- Deploy and run models on AWS (including Amazon Bedrock) and Azure
- Set up MLOps for training, versioning, deployment, monitoring, and rollback
- Build RAG pipelines: chunking, embeddings, retrieval, and grounding
- Measure quality with test sets, metrics, and regression checks before release
- Debug latency, cost, hallucination, and data issues in production
- Write clean, tested Python services and integrate them with existing backends
More Info
Key Skills
embeddings
LLM features
regression checks
test sets
chunking
Hugging Face models
retrieval
RAG pipelines
rollback
Python services
classical ML
prompts retrieval tool
Amazon Bedrock
About Company
Skills:
React, Typescript, Javascript, Azure, Python, Sql, AWS
Skills:
VMware, Azure Virtual Desktop, San, Nas, Networking, Citrix, Xenserver, Vsphere, Tcp/ip, Storage Subsystems, Active Directory, Security, windows networking, MS Hyper-V
Skills:
Python, Sql, Java, Automated Testing, Numpy, React, Pandas, AWS, FastAPI, Nosql, Kubernetes, Azure, Docker, Git, Javascript, Azure OpenAI, OpenAI Claude, CI/CD pipelines
Skills:
Pricing quoting of Telecom products, Tracking performance analytics, Sales Support, Cross-training systems and products, Demand templates for Pricing group engagement
Skills:
Sql, Javascript, Pytorch, Keras, Gcp, AWS, Kubernetes, python, Azure, Docker, Langchain, vLLM, Langgraph, Gen AI, Huggingface, DeepSpeed, Azure AI, tensor flow, Llm, Vertex AI, MLflow, Langflow, SageMaker, LLamaIndex, Flowise