AI Solution Architect : INR 40-50 LPA

Position Overview :

We are seeking an experienced AI Architect to join our dynamic team. This role combines deep technical expertise in traditional statistics, classical machine learning, and modern AI with full-stack development capabilities to build end-to-end intelligent systems. You'll work on revolutionary projects involving generative AI, large language models, and advanced data science applications.

Location and Type :

Bangalore ( Whitefield ) with hybrid (3 days / week)

Timings : 2-11 pm

Key Responsibilities :

a) AI / ML Development & Data Science :

Design, develop, and deploy machine learning models ranging from classical algorithms to deep learning for production environments

Apply traditional statistical methods including hypothesis testing, regression analysis, time series forecasting, and experimental design

Build and optimize large language model applications including fine-tuning, prompt engineering, and model evaluation

Implement Retrieval Augmented Generation (RAG) systems for enhanced AI capabilities

Conduct advanced data analysis, statistical modeling, A / B testing, and predictive analytics using both classical and modern techniques

Research and prototype cutting-edge generative AI solutions

b) Traditional ML & Statistics :

Implement classical machine learning algorithms including linear / logistic regression, decision trees, random forests, SVM, clustering, and ensemble methods

Perform feature engineering, selection, and dimensionality reduction techniques

Conduct statistical inference, confidence intervals, and significance testing

Design and analyze controlled experiments and observational studies

Apply Bayesian methods and probabilistic modeling approaches

c) Full Stack Development :

Develop scalable front-end applications using modern frameworks (React, Vue.js, Angular)

Build robust backend services and APIs using Python, Node.js, or similar technologies

Design and implement database solutions (SQL / NoSQL) optimized for ML workloads

Create intuitive user interfaces for AI-powered applications and statistical dashboards

d) MLOps & Infrastructure :

Establish and maintain ML pipelines for model training, validation, and deployment

Implement CI / CD workflows for ML models using tools like MLflow, Kubeflow, or similar

Monitor model performance, drift detection, and automated retraining systems

Deploy and scale ML solutions using cloud platforms (AWS, GCP, Azure)

Containerize applications using Docker and orchestrate with Kubernetes

e) Collaboration & Leadership :

Work closely with data scientists, product managers, and engineering teams

Mentor junior engineers and contribute to technical decision-making

Participate in code reviews and maintain high development standards

Stay current with latest AI / ML trends and technologies

Required Qualifications :

a) Experience & Education :

7-8 years of professional software development experience

Bachelor's or Master's degree in Computer Science, Statistics, Mathematics, Machine Learning, Data Science, or related field

6+ years of hands-on AI / ML experience in production environments

b) Technical Skills :

Programming : Expert proficiency in Python, strong experience with JavaScript / TypeScript, R is a plus

Traditional ML : Scikit-learn, XGBoost, LightGBM, classical algorithms and ensemble methods

Statistics : Hypothesis testing, regression analysis, ANOVA, time series analysis, experimental design, Bayesian inference

Statistical Tools : Experience with R, SAS, SPSS, or similar statistical software packages

Deep Learning : TensorFlow, PyTorch, neural networks, computer vision, NLP

LLM Experience : Working with GPT, Claude, Llama, or similar models; experience with fine-tuning and prompt engineering

RAG Implementation : Vector databases (Pinecone, Weaviate, Chroma), embedding models, semantic search

Data Science : Pandas, NumPy, statistical analysis, data visualization (Matplotlib, Plotly, Seaborn), feature engineering

Full Stack : React / Vue.js, Node.js / FastAPI, REST / GraphQL APIs

Databases : PostgreSQL, MongoDB, Redis, vector databases

MLOps : Docker, Kubernetes, CI / CD, model versioning, monitoring tools

Cloud Platforms : AWS / GCP / Azure, serverless architectures

c) Soft Skills :

Strong problem-solving and analytical thinking

Excellent communication and collaboration abilities

Self-motivated with ability to work in fast-paced environments

Experience with agile development methodologies

d) Preferred Qualifications

Experience with causal inference methods and econometric techniques

Knowledge of distributed computing frameworks (Spark, Dask)

Experience with edge AI and model optimization techniques

Publications in AI / ML / Statistics conferences or journals

Open source contributions to ML / statistical projects

Experience with advanced statistical modeling and multivariate analysis

Familiarity with operations research and optimization techniques