AI Solution Architect: INR 40-50 LPA

Full Description

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