V
Data Scientist
V
- Posted 12 hours ago
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Job Description
Job Requirements
Role Summary:
7-10Years
Role Summary:
- We need a skilled Senior AI Engineer to build and scale enterprise-grade Generative AI solutions.
- You will design retrieval-augmented generation (RAG) pipelines, fine-tune large language models, and build robust data architectures, bridging the gap between heavy data engineering and advanced AI model deployment.
- Design and deploy end-to-end GenAI and LLM-based applications into production.
- Build scalable RAG architectures using vector databases and semantic search.
- Optimize data pipelines to ingest, clean, and process multi-source data for AI models.
- Fine-tune, evaluate, and prompt-engineer open-source and proprietary LLMs.
- Integrate AI services with cloud infrastructure and existing enterprise applications.
- Monitor model performance, latency, cost, and safety guardrails.
- Minimum 7 to 10 years of experience in IT Industry with 3 to 5 years in AI, LLM implementation, and Data Engineering
- Generative AI & LLMs: RAG frameworks (LangChain, LlamaIndex), vector databases (Pinecone, Milvus, Qdrant), prompt engineering, and LLM fine-tuning.
- Data Engineering: Python, SQL, ETL pipelines, Apache Spark, Kafka, and data warehousing.
- Cloud & MLOps: AWS, GCP, or Azure; Docker, Kubernetes, and MLflow/Kubeflow.
- Software Engineering: Clean code practices, REST APIs, and microservices architecture
7-10Years
More Info
Key Skills
RAG frameworks
Qdrant
vector databases
MLflow
Pinecone
Kubeflow
Cloud MLOps
ETL pipelines
LangChain
Generative AI
LLMs
Clean code practices
prompt engineering
LLM fine-tuning
microservices architecture
Milvus
LlamaIndex
About Company
Skills:
Large Language Models (LLMs), Machine Learning, Artificial Intelligence, Pyspark, Deep Learning, Git, Bitbucket, Predictive Analytics, Generative AI, Advanced Python, Model Deployment and Monitoring, Data Pipeline Development, GitHub Copilot, Automated Unit Testing, Python Packaging and Deployment, Statistical Modeling, CI CD and DevOps Practices, Data Processing and Transformation, Causal AI, Data Preparation for ML Workloads, Scalable and Performant Application Design, Knowledge Graphs
Skills:
Gcp, Pyspark, Sklearn, Tensorflow, Azure, Python, Pytorch
Skills:
metaheuristics , Pyspark, Sql, Deep Learning, Python, Column Generation, LP, MILP, heuristics, AI Coding Agents, CPLEX, Decomposition, Pyomo, OR-Tools, Pulp, constraint programming, Network Flows, HiGHS, CP-SAT, Operations Research, Simulation, Gurobi
Skills:
Matplotlib, Hadoop, Power Bi, data mining, Big Data Technologies, Tableau, Google Cloud, Spark, Azure, Python, Machine Learning Algorithms, AWS, data visualization tools, R, MLOps practices, Statistical Modeling, cloud-based data platforms, feature engineering
Skills:
Sql, Databricks, Python, vector search, AI Foundry, tool calling, deployment on Azure, Azure OpenAI, CI CD, Agents, RAG, Microsoft Fabric