Senior AI Engineer

Company Description

About the Role:

Key Responsibilities:

• Develop and Fine-Tune LLMs (e.g., GPT-4, Claude, LLaMA, Mistral, Gemini) using instruction tuning, prompt engineering, chain-of-thought prompting, and fine-tuning techniques.
• Build RAG Pipelines: Implement Retrieval-Augmented Generation solutions leveraging embeddings, chunking strategies, and vector databases like FAISS, Pinecone, Weaviate, and Qdrant.
• Implement and Orchestrate Agents: Utilize frameworks like MCP, OpenAI Agent SDK, LangChain, LlamaIndex, Haystack, and DSPy to build dynamic multi-agent systems and serverless GenAI applications.
• Deploy Models at Scale: Manage model deployment using HuggingFace, Azure Web Apps, vLLM, and Ollama, including handling local models with GGUF, LoRA/QLoRA, PEFT, and Quantization methods.
• Integrate APIs: Seamlessly integrate with APIs from OpenAI, Anthropic, Cohere, Azure, and other GenAI providers.
• Ensure Security and Compliance: Implement guardrails, perform PII redaction, ensure secure deployments, and monitor model performance using advanced observability tools.
• Optimize and Monitor: Lead LLMOps practices focusing on performance monitoring, cost optimization, and model evaluation.
• Work with AWS Services: Hands-on usage of AWS Bedrock, SageMaker, S3, Lambda, API Gateway, IAM, CloudWatch, and serverless computing to deploy and manage scalable AI solutions.
• Contribute to Use Cases: Develop AI-driven solutions like AI copilots, enterprise search engines, summarizers, and intelligent function-calling systems.
• Cross-functional Collaboration: Work closely with product, data, and DevOps teams to deliver scalable and secure AI products.
  

Required Skills and Experience:

• 3-5 years of experience in AI/ML roles, focusing on LLM agent development, data science workflows, and system deployment.
• Demonstrated experience in designing domain-specific AI systems and integrating structured/unstructured data into AI models.
• Proficiency in designing scalable solutions using LangChain and vector databases.
• Deep knowledge of LLMs and foundational models (GPT-4, Claude, Mistral, LLaMA, Gemini).
• Strong expertise in Prompt Engineering, Chain-of-Thought reasoning, and Fine-Tuning methods.
• Proven experience building RAG pipelines and working with modern vector stores (FAISS, Pinecone, Weaviate, Qdrant).
• Hands-on proficiency in LangChain, LlamaIndex, Haystack, and DSPy frameworks.
• Model deployment skills using HuggingFace, vLLM, Ollama, and handling LoRA/QLoRA, PEFT, GGUF models.
• Practical experience with AWS serverless services: Lambda, S3, API Gateway, IAM, CloudWatch.
• Strong coding ability in Python or similar programming languages.
• Experience with MLOps/LLMOps for monitoring, evaluation, and cost management.
• Familiarity with security standards: guardrails, PII protection, secure API interactions.
• Use Case Delivery Experience: Proven record of delivering AI Copilots, Summarization engines, or Enterprise GenAI applications.
  

• Experience in BPO or IT Outsourcing environments.
• Knowledge of workforce management tools and CRM integrations.
• Hands-on experience with AI technologies and their applications in data analytics.
• Familiarity with Agile/Scrum methodologies.
  

• Strong analytical and problem-solving capabilities.
• Excellent communication and stakeholder management skills.
• Ability to thrive in a fast-paced, dynamic environment.