Agentic Systems - FS

Infosys

63d•$154,375 - $193,125•Onsite

About The Position

We are seeking a hands-on Financial Services Principal Consultant with AI experience to design, build, and scale GenAI-powered solutions for banking and financial services. The role focuses on understanding business pain points, define solution leveraging AI techniques, with experience in LLM application development, agent orchestration, and data-driven AI pipelines to be developed for specific financial services use cases. Here are some AI engagements our consultants are working on: Improve Day in life of Wealth Advisor leveraging AI interventions such as – Client Profiling, Client Advisor Mapping, Meeting summarization, Next Best Action. Reduce false positives in Trade / AML Surveillance to ensure Compliance personnel are focusing their time on critical alerts. Faster onboarding of clients leveraging AI driven KYC processes – Negative News screening, Beneficial Ownership determination, Document information extraction and validation. Improve Operational processes leveraging AI / Gen AI – Corporate Actions, Reconciliations, Predict Settlement failures, etc Digitalization of Advisory Platforms covering end to end Advisory lifecycle from Prospecting to Portfolio Management Machine Learning driven personalization - life events, investment ideas, service alerts and assessment of client attrition risk Virtual Assistant - Cognitive Assistant Pilot (CAP) to guide Financial Advisors and Customer Support Associates in Salesforce. Comprehensive portal to do a “Best Match” and find a Financial Advisor (Individual or Teams) by name, nearby location, gender and International Wealth FAs and teams

Requirements

Experience in financial services industry (Capital markets / Banking) and familiarity with Front office / Middle office / Back office operations or processes
Understanding of the end to end value chain (wealth management, asset management, investment banking, etc.)
Experience with defining AI related KPIs and building AI adoption business case
Experience in Python-based AI/ML development
Understanding of: LLMs and GenAI architectures, Prompt engineering and chaining techniques, Data processing and pipelines
Understanding of Graph RAG and hands-on with one or more: LangChain / similar frameworks, Claude / OpenAI / other LLM APIs
Working knowledge of: Vector databases, embeddings, semantic search, API integrations and microservices
Bachelor’s degree or equivalent required
Prior work experience of 8-12a years either in management consulting or directly working on advisory business, technology or operational change initiatives in Financial Services industry.
Strong problem-solving mindset
Ability to work in fast-paced AI delivery pods
Ability to travel up to 75%.

Nice To Haves

Exposure to Google ADK / agentic frameworks
Experience with knowledge graphs and graph-based retrieval
Understanding of banking / financial services domain
Familiarity with cloud platforms (Azure / AWS / GCP)
Data engineering basics (SQL, schema, profiling)
Ability and willingness to work as part of a cross-cultural team including flexibility to support multiple time zones when necessary
Candidates should be able to ramp up quickly with minimal support, take full ownership of assigned tasks, and consistently deliver against timelines and commitments.

Responsibilities

Work with business stakeholders (Banking and financial services) to identify the problem statement and participate in / lead solution design. E.g. Understand Day in life of an Advisor, identify critical manual flows and define potential solutions
Act as a Product Manager / Product Owner and work closely with architects, business SMEs, to develop business requirements and AI solutions
Design and develop GenAI solutions using LLMs (Claude, OpenAI, etc.) to solve business problems such as Improving Advisor productivity, Operations efficiency in areas such as Corporate Actions, Portfolio Mgmt., AML / KYC processes.
Build agent-based workflows using frameworks such as LangChain / Google ADK / similar
Develop RAG pipelines, semantic search, and knowledge-driven applications
Implement prompt engineering, evaluation frameworks, and guardrails
Perform testing, validation, of final solution to ensure conformity to business problems and optimization of AI models and pipelines
Coordinate with business and technology stakeholders for program delivery