Lead Data Scientist - Bangalore

Allegis Global Solutions (AGS); Bengaluru; Hybrid; INR 1,200,000 - 2,400,000; Full time

Job summary

Allegis Global Solutions (AGS) is seeking a senior ML/AI engineer to design and deploy production-grade ML and LLM systems. You will lead data processing, validation, and pipeline development to drive enterprise AI initiatives.

Responsibilities include building end-to-end ML pipelines, applying statistical models, and leveraging frameworks like LangChain, AutoGen, and vector databases. Strong focus on observability and API-first architecture will be required.

Qualifications

• Proficiency in Python, ML, REST API development, FastAPI, and SQL.
• Experience designing and deploying production-grade ML/LLM systems in enterprise environments.
• Strong data processing, cleansing, validation, and data integrity for analysis and model development.
• Data quality checks, exploratory analysis, and performance evaluation to guide design.
• End-to-end ML pipelines, including data structures, transformations, and deployment.
• Strong understanding of statistical modeling techniques and ML algorithms.

Skills

Python

Machine Learning

REST API development

FastAPI

SQL

ML pipelines

Statistical modelling

ML algorithms

LLMs

Prompt engineering

Vector databases

Embeddings

Retrieval pipelines

LangChain

Semantic Kernel

AutoGen

Observability

APIs-first design

Microservices

Tools

LangChain

Semantic Kernel

AutoGen

Vector databases

Job description

Role & responsibilities

Mandatory Skills:

• Proficiency in Python, Machine Learning, REST API development, FastAPI, and SQL.
• Experience designing and deploying production-grade ML and LLM systems in enterprise environments.
• Strong experience with data processing, cleansing, validation, and ensuring data integrity for analysis and model development.
• Conduct data quality checks, exploratory analysis, and performance evaluation to guide model and system design.
• Demonstrated expertise in building end-to-end ML pipelines, including data structures, transformations, and deployment pipelines.
• Strong understanding of statistical modelling techniques such as Regression, Clustering, Decision Trees, and Logistic Regression.
• Familiarity with machine learning algorithms such as KNN, Random Forests, Ensemble Methods, and probabilistic models.
• Experience with Large Language Models such as GPT, BERT, or similar architectures, including prompt engineering and solution design.
• Hands-on experience with vector databases, embeddings, and retrieval pipelines.
• Familiarity with agent frameworks and orchestration patterns such as LangChain, Semantic Kernel, AutoGen, or similar.
• Knowledge of observability and monitoring tools for AI systems, including logging, tracing, and telemetry.
• Strong understanding of microservices architecture and API-first design principles.