Engineer / Senior Engineer - AI Engineering

Cognicor; Ernakulam; On-site; INR 1,500,000 - 2,500,000; Full time

Job summary

Cognicor in Ernakulam, Kerala is searching for AI Engineers to design next-generation AI copilots for wealth management and financial advisory. The role requires strong expertise in LLM systems, building intelligent assistants, and integrating AI into enterprise structures. Candidates should have 6–10 years in software engineering, including 3+ years in AI/ML. Strong knowledge of Python, AI architectures, and collaboration with cross-functional teams is essential. The company offers full-time positions with competitive growth opportunities.

Qualifications

• 6–10 years of experience in software engineering.
• 3+ years in AI/ML and LLM-based systems.
• Demonstrated ability to deliver end-to-end AI products.

Responsibilities

• Design and build AI copilots for financial advisors.
• Optimize latency, cost, and response quality of LLM systems.
• Integrate AI into enterprise ecosystems like CRM.

Skills

Software engineering

AI/ML

LLM-based systems

Prompt engineering

API integration

Collaboration

Education

Bachelor's Degree in Computer Science or related field

Tools

Python

Azure

OpenAI

LangChain

CI/CD pipelines

Salesforce

Job description

3-6

Full-Time

About the Role

We are looking for AI Engineers to design and build next‑generation AI copilots and agentic systems for the wealth management and financial advisory domain.

You will work at the intersection of LLMs, financial workflows, and user experience, building intelligent assistants that augment advisors, automate workflows, and enhance client engagement.

This role requires strong hands‑on expertise in LLM systems, deep understanding of enterprise‑grade AI architecture, and the ability to translate complex financial use cases into scalable AI products.

What You Will Do

• Design and build AI copilots Architect and develop AI copilots for financial advisors, client servicing, and internal operations
• Build multi‑step agentic workflows using LLMs, tools, memory, and orchestration frameworks
• Develop production‑grade LLM systems Implement Retrieval‑Augmented Generation (RAG), semantic search, and grounded responses
• Design prompt strategies, evaluation pipelines, and guard rails for accuracy and compliance
• Own end‑to‑end AI solution lifecycle From PoC to production deployment with scalability, reliability, and observability
• Optimize latency, cost, and response quality of LLM systems
• Integrate AI into enterprise ecosystems Embed copilots into CRM, advisor platforms, communication tools, and workflow systems
• Work with APIs, event‑driven systems, and microservices
• Collaborate cross‑functionally Partner with Product, UX, and domain experts in wealth management
• Translate advisor and client needs into AI capabilities and user journeys
• Drive AI innovation Evaluate emerging LLM models, agent frameworks, and tooling
• Contribute to AI architecture standards, reusable components, and internal platforms

What We Are Looking For

Core Experience

• 6–10 years in software engineering, with 3+ years in AI/ML and LLM‑based systems
• Strong experience building LLM‑powered applications in production environments
• Proven track record of delivering end‑to‑end AI products, not just experiments

Technical Skills

• Languages: Python (preferred), TypeScript or C#
• LLM & AI Stack: OpenAI / Azure OpenAI / Anthropic or equivalent
• RAG architectures, embeddings, vector databases: Pinecone, Qdrant, Azure AI Search
• Prompt engineering, evaluation frameworks, hallucination control
• Agentic Frameworks: LangChain, LlamaIndex, CrewAI, or similar orchestration frameworks
• Experience designing multi‑agent or tool‑using systems
• Cloud & Architecture: Azure (preferred) or AWS/GCP
• Microservices, APIs, event‑driven systems
• Experience with scalable, distributed systems
• DevOps & Production Readiness: CI/CD pipelines, monitoring, logging, and observability
• Model evaluation, A/B testing, and performance tuning

Domain & Product Orientation

• Strong interest or experience in wealth management, financial advisory, or fintech
• Understanding of:
  • Client‑advisor workflows
  • Financial documents, compliance constraints, and data sensitivity
• Ability to build user‑centric AI systems, not just backend models

Preferred Experience

• Experience building AI copilots or conversational assistants in enterprise settings
• Exposure to Microsoft ecosystem (Azure AI, Microsoft 365, Teams, Graph API)
• Experience integrating with CRM systems (e.g., Salesforce) and workflow automation tools
• Familiarity with evaluation frameworks, guard rails, and responsible AI practices
• Prior work in AI platforming, reusable AI components, or AI CoE setups

What Success Looks Like

• AI copilots are actively used by advisors and internal teams
• Solutions demonstrate high accuracy, low hallucination, and strong adoption
• Systems are scalable, secure, and compliant with financial domain requirements
• Clear impact on productivity, client experience, and business outcomes

Required Skills

LLM Artificial Intelligence Platforms prompt engineering