AI ML Engineer

Job description

Experience :5 to 8 Years
Job Location :Bangalore
Mandatory skills: Python, Gen AI, traditional ML, core Data scientist, ML Ops, Agentic AI
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.
Key Responsibilities
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
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
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
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
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
Experience & Education:5 -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
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
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
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

Required Skill Profession

Computer Occupations