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