Data Scientist

mk Construction; Dadri, Cyber City, Bengaluru; Hybrid; INR 600,000 - 1,100,000; Full time

Job summary

mk Construction seeks an AI/ML engineer to design, train, and deploy machine learning and deep learning models for business use cases. You will build Generative AI applications using LLMs, and create RAG-based systems that leverage embeddings, vector databases, and contextual generation.

You will collaborate with software engineers to deploy AI solutions via REST APIs and cloud-based infrastructure, with focus on scalable ML/AI pipelines and responsible AI practices.

Qualifications

• Proficient in Python, SQL, statistics and data analysis.
• Hands-on experience with Scikit-learn, Pandas, NumPy and XGBoost/LightGBM.
• Experience with supervised and unsupervised machine learning.
• Understanding of model evaluation, experimentation, feature engineering, and model optimization.

Responsibilities

• Design, develop, train, evaluate, and deploy ML and DL 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 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 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.

Skills

Python

SQL

Statistics

Scikit-learn

Pandas/NumPy

XGBoost/LightGBM

PyTorch

TensorFlow

NLP/Transformers

LangChain

LangGraph

LlamaIndex

APIs/SDKs for LLMs

Tools

Docker

Git

CI/CD

MLflow

Kubeflow

REST APIs

Job description

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