Senior AI Engineer

Quantanite is a customer experience (CX) solutions company that helpsfast-growing companies
and leading global brandsto transformand grow. We do this through a collaborative and
consultative approach,rethinking business processes and ensuring our clients employ the
optimalmix of automation and human intelligence.

We are an ambitiousteamof professionals
spread acrossfour continents and looking to disrupt our industry by delivering seamless
customerexperiencesforour clients,backed-upwithexceptionalresults.

We havebig dreams,
and are constantly looking for new colleagues to join us who share our values, passion and
appreciationfordiversity.

About the Role:

We are seeking a highly skilled Senior AI Engineer with deep expertise in Agentic frameworks, Large Language Models (LLMs), Retrieval-Augmented Generation (RAG) systems, MLOps/LLMOps, and end-to-end GenAI application development. In this role, you will design, develop, fine-tune, deploy, and optimize state-of-the-art AI solutions across diverse enterprise use cases including AI Copilots, Summarization, Enterprise Search, and Intelligent Tool Orchestration.

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, Lang Chain, Llama Index, Haystack, and DSPy to build dynamic multi-agent systems and serverless GenAI applications.

Deploy Models at Scale:
Manage model deployment using Hugging Face, 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, Sage Maker, S3, Lambda, API Gateway, IAM, Cloud Watch, 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 Dev Ops 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 Lang Chain 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 Lang Chain, Llama Index, Haystack, and DSPy frameworks.

Model deployment skills using Hugging Face, vLLM, Ollama, and handling LoRA/QLoRA, PEFT, GGUF models.

Practical experience with AWS serverless services:
Lambda, S3, API Gateway, IAM, Cloud Watch.

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.