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AI ML Engineer at Weekday AI
Bengaluru, karnataka, India -
Full Time

Start Date

Immediate

Expiry Date

05 Mar, 26

Salary

2000000.0

Posted On

05 Dec, 25

Experience

5 year(s) or above

Remote Job

Yes

Telecommute

Yes

Sponsor Visa

No

Skills

ML Pipeline Design, APIs Development, GPU Architecture, Distributed Training, Model Fine-Tuning, DevOps Practices, Kubernetes, Docker, Python, SQL, AWS, Azure, GCP, MLflow, SageMaker, Hugging Face, DeepSpeed

Industry

technology;Information and Internet

Description
This role is for one of the Weekday's clients Salary range: Rs 800000 - Rs 2000000 (ie INR 8-20 LPA) Min Experience: 6 years Location: Bengaluru, Karnataka, NCR, Delhi JobType: full-time Key Responsibilities: ML Pipeline Design Design and implement scalable ML pipelines for experiment tracking, model management, feature management, and model retraining. Develop APIs for high-performance model inferencing. Hands-on experience with MLflow, SageMaker, Vertex AI, and Azure AI. LLM Serving & GPU Architecture Deep understanding of GPU architectures for ML workloads. Expertise in distributed training and serving of large language models (LLMs). Skilled in model/data parallel training using frameworks such as DeepSpeed and serving frameworks like vLLM. Model Fine-Tuning & Optimization Fine-tune and optimize LLM and LVM models for improved latency and accuracy. Reduce training time and resource requirements for large-scale model deployments. DevOps & LLMOps Proficient in DevOps and LLMOps practices, including Kubernetes, Docker, and container orchestration. Experience with LLM orchestration frameworks such as Flowise, Langflow, and Langgraph. Technical Skills & Tools: LLM Frameworks: Hugging Face OSS LLMs, GPT, Gemini, Claude, Mixtral, Llama LLMOps Tools: MLflow, Langchain, Langgraph, LangFlow, Flowise, LlamaIndex, SageMaker, AWS Bedrock, Vertex AI, Azure AI Databases/Data Warehouses: DynamoDB, Cosmos, MongoDB, RDS, MySQL, PostgreSQL, Aurora, Spanner, Google BigQuery Cloud Platforms: AWS, Azure, GCP DevOps Tools: Kubernetes, Docker, FluentD, Kibana, Grafana, Prometheus Programming & Scripting: Python, SQL, JavaScript Bonus: AWS Professional Solutions Architect, AWS Machine Learning Specialty, Azure Solutions Architect Expert
Responsibilities
Design and implement scalable ML pipelines for experiment tracking and model management. Fine-tune and optimize large language models for improved performance and efficiency.