Description

Key Responsibilities

• Design, develop, and deploy scalable AI/ML solutions to address complex business challenges.
• Build and optimize Machine Learning models using industry-standard methodologies and evaluation techniques.
• Develop Agentic AI applications utilizing frameworks such as LangChain, AutoGen, and CrewAI.
• Design and implement Retrieval-Augmented Generation (RAG) pipelines using vector databases and embedding models.
• Integrate Large Language Models (LLMs) into enterprise applications and workflows.
• Evaluate model performance using appropriate metrics and continuously improve solution effectiveness.
• Collaborate with cross-functional teams to translate business requirements into AI-driven solutions.
• Ensure scalability, reliability, and performance of deployed AI systems.

Required Qualifications

• Bachelor's or Master's degree in Computer Science, Data Science, Artificial Intelligence, Statistics, or a related field.
• 4+ years of experience in Machine Learning, Artificial Intelligence, or Data Science.
• Strong understanding of statistical concepts and machine learning algorithms.
• Proficiency in Python and related AI/ML libraries such as Scikit-Learn, Pandas, NumPy, TensorFlow, or PyTorch.
• Hands-on experience with Generative AI and Agentic AI frameworks, including:
• LangChain
• AutoGen
• CrewAI
• Experience building and deploying RAG-based solutions.
• Experience working with vector databases such as:
• Pinecone
• ChromaDB
• FAISS
• Weaviate
• Milvus
• Strong understanding of LLM architectures, prompt engineering, embeddings, and semantic search.
• Experience integrating OpenAI, Anthropic Claude, Gemini, Llama, or equivalent foundation models.

Machine Learning & Statistics Expertise

Candidates should demonstrate proficiency in:

• Supervised and Unsupervised Learning techniques.
• Model training, validation, and optimization.
• Feature Engineering and Data Preprocessing.
• Statistical Analysis and Hypothesis Testing.
• Model Evaluation Metrics, including:
• Precision
• Recall
• F1 Score
• ROC-AUC
• Accuracy
• Confusion Matrix Analysis

Preferred Qualifications

• Experience with AWS, Azure, or Google Cloud Platform.
• Knowledge of MLOps, CI/CD pipelines, and model deployment strategies.
• Experience with AI monitoring, observability, and evaluation frameworks.
• Experience developing enterprise-scale AI solutions in production environments.
• Familiarity with API development and microservices architecture.

Required Project Experience

Candidates should be able to demonstrate at least one production implementation involving:

• Agentic AI or Multi-Agent Systems.
• Retrieval-Augmented Generation (RAG) Architecture.
• Vector Database Integration.
• LLM-Based Application Development.
• Quantifiable business outcomes, such as:
• Increased operational efficiency
• Process automation
• Cost reduction
• Improved accuracy and response quality
• Enhanced user productivity

Key Skills

Education

Bachelor's or Master's degrees