Senior Associate - Data Science

Job Description & Summary: · Proficient in programming languages like R, Python, and database query languages like SQL, Hive, Pig is desirable. Familiarity with Scala, Java, or C++ is an added advantage. · Proficient in Statistical modelling and Machine techniques such as time series forecasting, Reliability models, Markov Models, Stochastic models, Bayesian Modelling, Classification Models, Cluster Analysis, Neural Network, etc. · Good exposure to deep learning and associated frameworks (PyTorch, TensorFlow, and Keras). · Ability to perform preprocessing of structured and unstructured data: Processing, cleansing, and validating the integrity of data to be used for analysis. · Strong working experience with cloud platforms: build, train, and deploy ML Models on Azure/AWS/GCP. · Good working knowledge in distributed computing environments / big data platforms (Hadoop, Elasticsearch, etc.) as well as common database systems and value stores (SQL, Hive, HBase, etc.). · Hand-on experience in the MLOps (Dockerization, REST APIs and the CI/CD/CT processes). · Adaptation of foundation models/LLMs to address specific business challenges. · Utilizing version control for maintaining codebase integrity and collaboration, fostering a collaborative and error-free development environment. · Design, deploy and manage prompt-based models on LLMs for various NLP tasks. · Build and maintain data pipelines and data processing workflows for prompt engineering on LLMs utilizing cloud services for scalability and efficiency. · Familiarity with LLM orchestration and agentic AI libraries. · Good understanding of business and ability to translate domain problems to data science problem. · Ability to communicate effectively with both technical and non-technical stakeholders.

Responsibilities: · ML Pipeline Design: Design ML pipelines for experiment management, model management, feature management, and model retraining. Design APIs for model inferencing at scale. Proven expertise with MLflow, SageMaker, Vertex AI, and Azure AI. LLM Serving and GPU Architecture: Possess deep knowledge of GPU architectures. Expertise in distributed training and serving of large language models. Proficient in model and data parallel training using frameworks like DeepSpeed and service frameworks like vLLM. Model Fine-Tuning and Optimization: Demonstrate proven expertise in model fine-tuning and optimization techniques. Achieve better latencies and accuracies in model results. Reduce training and resource requirements for fine-tuning LLM and LVM models. DevOps and LLMOps Proficiency: Proven expertise in DevOps and LLMOps practices. Knowledgeable in Kubernetes, Docker, and container orchestration. Deep understanding of LLM orchestration frameworks like Flowise, Langflow, and Langgraph. Skill Matrix LLM: Hugging Face OSS LLMs, GPT, Gemini, Claude, Mixtral, Llama LLM Ops: ML Flow, Langchain, Langraph, LangFlow, Flowise, LLamaIndex, SageMaker, AWS Bedrock, Vertex AI, Azure AI Databases/Datawarehouse: DynamoDB, Cosmos, MongoDB, RDS, MySQL, PostGreSQL, Aurora, Spanner, Google BigQuery. Cloud Knowledge: AWS/Azure/GCP Dev Ops (Knowledge): Kubernetes, Docker, FluentD, Kibana, Grafana, Prometheus Cloud Certifications (Bonus): AWS Professional Solution Architect, AWS Machine Learning Specialty, Azure Solutions Architect Expert Proficient in Python, SQL, Javascrip

Mandatory skill sets: • · • Gen AI,LLM, Huggingface, python,pytorch/tensor flow/keras, Langchain, Langgraph, Docker, Kunernetes

Preferred skill sets: · • Gen AI,LLM, Huggingface, python,pytorch/tensor flow/keras, Langchain, Langgraph, Docker, Kunernetes

Years of experience required: 5-8 years

Education qualification: B.Tech/MCA/BCA/M.tech