m
mk Construction
Data Scientist
Skills
Google Cloud PlatformSQL QueriesMongoDBData EngineeringDeep LearningDockerPythonData ScienceMicroservicesNumPypandasPyTorchTensorFlowPostgreSQLMySQLData AnalysisMachine LearningAIAWSNLP / Natural Language ProcessingAzure
2 months ago
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Important information
Contract type:
Permanent
Salary:
₹70,000 – ₹125,000 / year
Location:
Bengaluru, Karnataka, India
Work mode:
Hybrid
Published on:
7 September 2026
What they need
- Key Responsibilities
- Design, develop, train, evaluate, and deploy machine learning and deep learning models for business use cases.
- Develop and productionize Generative AI applications using LLMs and foundation models.
- Build RAG-based applications using embeddings, vector databases, document processing, retrieval, reranking, and contextual generation.
- Design and implement Agentic AI systems capable of planning, reasoning, tool usage, task execution, and multi-step decision-making.
- Develop AI agents using frameworks such as LangChain, LangGraph, LlamaIndex, AutoGen, or equivalent technologies.
- Integrate LLMs with external APIs, databases, enterprise systems, search engines, and internal tools.
- Implement prompt engineering, structured outputs, function/tool calling, memory, context management, and agent orchestration.
- Fine-tune or adapt LLMs using techniques such as LoRA/PEFT, instruction tuning, and supervised fine-tuning, where appropriate.
- Develop NLP and ML solutions including text classification, information extraction, semantic search, recommendation, forecasting, and anomaly detection.
- Design experiments and evaluate models using appropriate offline and online evaluation metrics.
- Implement GenAI evaluation frameworks to measure accuracy, relevance, groundedness, hallucination, latency, and cost.
- Perform data preprocessing, feature engineering, exploratory data analysis, statistical analysis, and model selection.
- Build scalable ML/AI pipelines for data ingestion, training, evaluation, deployment, and monitoring.
- Optimize LLM applications for performance, reliability, latency, scalability, and inference cost.
- Implement responsible AI practices including guardrails, security, privacy, hallucination mitigation, and prompt-injection protection.
- Collaborate with software engineers to deploy AI solutions through REST APIs, microservices, and cloud-based infrastructure.
- Monitor production models and AI applications and continuously improve their performance.
- Communicate technical findings and AI-driven insights to both technical and non-technical stakeholders.
- Required Skills
- Data Science & Machine Learning
- Strong knowledge of Python, SQL, statistics, probability, and data analysis.
- Hands-on experience with Scikit-learn, Pandas, NumPy, XGBoost/LightGBM, or equivalent.
- Experience with supervised and unsupervised machine learning.
- Understanding of model evaluation, experimentation, feature engineering, and model optimization.
- Deep Learning & NLP
- Experience with PyTorch and/or TensorFlow.
- Strong understanding of NLP, Transformers, embeddings, attention mechanisms, and deep learning architectures.
- Experience working with transformer-based models such as BERT, T5, Llama, Mistral, or equivalent models.
- Generative AI
- Hands-on experience developing applications using LLMs and Generative AI.
- Strong understanding of:
- Prompt engineering
- RAG
- Vector embeddings
- Vector databases
- Semantic search
- Function/tool calling
- Structured generation
- LLM evaluation
- Hallucination mitigation
- Experience with APIs/SDKs from major LLM providers or open-source models.
- Agentic AI
- Practical experience building AI agents and multi-step agentic workflows.
- Understanding of:
- Agent orchestration
- Planning and reasoning
- Tool calling
- Agent memory
- Multi-agent workflows
- Workflow/state management
- Human-in-the-loop systems
- Agent evaluation and observability
- Experience with LangGraph, LangChain, LlamaIndex, AutoGen, CrewAI, or similar frameworks is preferred.
- Cloud & MLOps
- Experience deploying ML/AI solutions using AWS, Azure, or GCP.
- Understanding of Docker, Git, CI/CD, APIs, and cloud deployment.
- Familiarity with ML lifecycle tools such as MLflow, Kubeflow, or equivalent.
- Experience with monitoring, logging, model versioning, and production troubleshooting.
- Databases & Data Engineering
- Strong SQL skills and experience with relational databases.
- Experience with PostgreSQL, MySQL, MongoDB, or equivalent databases.
- Familiarity with vector databases such as FAISS, Pinecone, Weaviate, Qdrant, or Chroma.
- Understanding of data pipelines and large-scale data processing
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