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About This Role
Must Have Technical/Functional Skills
Primary Skil: high\-impact GenAI Consultant to support US banking clients by shaping technology
Point\-of\-Views (POVs), translating emerging GenAI trends into real banking use cases, and presenting solutions to senior customer executives (CXO, EVP, SVP levels).
Experience: Minimum 10\+ years
Roles \& Responsibilities
GenAI Consultant – Banking \& Financial Services (US)
Role Overview
We are seeking a high\-impact GenAI Consultant to support US banking clients by shaping technology
Point\-of\-Views (POVs), translating emerging GenAI trends into real banking use cases, and presenting solutions to senior customer executives (CXO, EVP, SVP levels).
This role combines deep technology understanding, banking domain expertise, and strong executive communication skills to influence client decisions and drive AI\-led transformation initiatives.
Key Responsibilities
1\. Technology POV \& Thought Leadership
- Create compelling POVs, whitepapers, and narratives on GenAI, Agentic AI, Responsible AI, and AI\-driven modernization for the banking industry
- Track technology evolution (LLMs, multimodal AI, autonomous agents, AI platforms) and articulate business impact for banks
- Contribute to sales and account strategy by shaping differentiated AI positioning for US banking clients
- Openness to experiment and learn AI skills as per the emerging industry trends
2\. Banking Use\-Case Ideation \& Application
- Identify and define high\-value GenAI use cases across:
o Retail \& Commercial Banking
o Lending, Payments, Treasury
o Risk, Compliance, AML, Fraud
o Customer Service \& Operations
- Translate business problems into practical AI solution concepts, architectures, and roadmaps
- Work closely with architects, business domain experts, data scientists, and delivery teams to refine solution feasibility
3\. Customer Executive Engagement
- Present AI POVs, demos, and solution approaches to bank senior executives
- Lead client workshops, ideation sessions, and executive briefings
- Clearly articulate value, risk, governance, and ROI of GenAI adoption in regulated environments
4\. Pre\-sales \& Advisory Support
- Partner with sales and account leadership on client pursuits, RFPs, and solution shaping
- Support development of executive decks, proposals, and business cases
- Act as a trusted advisor to client stakeholders on GenAI strategy and adoption
Required Qualifications Experience
- Atleast 10\-15 years of experience in Technology Consulting, Digital Transformation, and AI\-led initiatives
- Strong experience working with US Banking / Financial Services clients
- Proven track record of executive\-level client presentations and discussions
Technical \& Domain Skills
- Strong understanding of GenAI/AI technologies:
o Large Language Models (LLMs)
o Prompt eng ineering \& agent\-based architectures
o AI platforms \& cloud AI ecosystems
- Solid grasp of banking processes and regulatory considerations
- Ability to connect technology capabilities to business outcomes
Communication \& Consulting Skills
· Excellent storytelling and executive communication skills · Ability to simplify complex AI concepts for non\-technical senior audiences
- Strong consulting mindset with client\-first problem solving
Good\-to\-Have Skills
- Experience working in onsite client\-facing roles in the US
- Prior involvement in AI strategy, responsible AI, or governance frameworks
- Experience creating industry POVs, whitepapers, or keynote content
- Exposure to cloud platforms (Azure/AWS/GCP) and enterprise architectures
What Success Looks Like
- Recognized by clients as a trusted GenAI advisor
- Clear, differentiated AI POVs that influence executive decisions
- Tangible impact on deal shaping, pipeline growth, and client transformation
- Strong collaboration with sales, delivery, and AI practice teams
TCS Employee Benefits Summary:
Discretionary Annual Incentive.
Comprehensive Medical Coverage: Medical \& Health, Dental \& Vision, Disability Planning \& Insurance, Pet Insurance Plans.
Family Support: Maternal \& Parental Leaves.
Insurance Options: Auto \& Home Insurance, Identity Theft Protection.
Convenience \& Professional Growth: Commuter Benefits \& Certification \& Training Reimbursement.
Time Off: Vacation, Time Off, Sick Leave \& Holidays.
Legal \& Financial Assistance: Legal Assistance, 401K Plan, Performance Bonus, College Fund, Student Loan Refinancing.
Salary Range: $120,000\- 145,000 a year
Location
Charlotte, NC
Job Function
TECHNOLOGY
Role
Consultant
Job Id
416744
Desired Skills
Artificial Intelligence \| Genesys
Salary Range
$120,000\-$145,000 a year
Desired Candidate Profile
Qualifications : BACHELOR OF COMPUTER SCIENCE
Role Details
About This Role
This role sits at the intersection of AI and engineering, building systems that bring machine learning capabilities into production environments. The scope varies by company, but the common thread is applying AI technology to solve real business problems at scale. Most AI roles today require a combination of software engineering fundamentals and domain-specific ML knowledge, with the exact mix depending on the team's maturity and the product they're building.
The AI job market is evolving fast. New role categories emerge as companies figure out what they need to ship AI-powered products. What matters most is the ability to learn quickly, build working systems, and iterate based on real-world performance data. The specific title matters less than the skills you bring and the problems you can solve. Companies are past the experimentation phase and want engineers who can deliver production-quality systems that work reliably at scale.
Across the 3,823 AI roles we're tracking, AI Consultant positions make up 0% of the market. At Tata Consultancy Services (TCS), this role fits into their broader AI and engineering organization.
AI hiring keeps growing across industries. Companies in tech, finance, healthcare, and retail are all building AI teams. The strongest demand is for people who can bridge the gap between AI research and production engineering. The shift toward generative AI has created new role types (LLM Engineer, Prompt Engineer, AI Agent Developer) that didn't exist three years ago, while traditional roles (Data Scientist, ML Engineer) have evolved to incorporate LLM capabilities.
What the Work Looks Like
Day-to-day work involves a mix of building, debugging, and collaborating. You'll write code, review pull requests, participate in design discussions, and work with cross-functional teams (product, design, data) to define what AI features should do and how they should behave. Expect to spend time on both technical implementation and communication. Most AI teams operate in two-week sprint cycles, with regular demos and retrospectives. The ratio of heads-down coding to meetings and reviews varies by seniority, with senior roles spending more time on architecture decisions and mentorship.
AI hiring keeps growing across industries. Companies in tech, finance, healthcare, and retail are all building AI teams. The strongest demand is for people who can bridge the gap between AI research and production engineering. The shift toward generative AI has created new role types (LLM Engineer, Prompt Engineer, AI Agent Developer) that didn't exist three years ago, while traditional roles (Data Scientist, ML Engineer) have evolved to incorporate LLM capabilities.
Skills Required
Python and cloud platform experience are common requirements. Specific skill needs vary by company and focus area, but familiarity with ML frameworks, data pipelines, and API design covers the basics for most roles. RAG (Retrieval-Augmented Generation), vector databases, and LLM API integration are increasingly standard requirements across role types.
Beyond the core stack, communication skills matter more than many technical candidates realize. The ability to explain AI capabilities and limitations to non-technical stakeholders is a differentiator at every level. Technical writing, documentation, and clear thinking about tradeoffs are underrated skills in AI roles. Experience with evaluation methodology (how to measure whether an AI system is working well) is becoming a core requirement, especially for roles that involve LLM integration.
Look for job postings that specify the problems you'll work on, the tech stack, and the team structure. Vague postings that list every AI buzzword are often a sign the company hasn't figured out what they need. Strong postings describe the product context, the team you'd join, and the specific challenges you'd tackle.
Compensation Benchmarks
AI Consultant roles pay a median of $205,800 based on 55 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $227,400. This role's midpoint ($132K) sits 36% below the category median. Disclosed range: $120K to $145K.
Across all AI roles, the market median is $200,100. Top-quartile compensation starts at $253,500. The 90th percentile reaches $307,500. For comparison, the highest-paying categories include AI Engineering Manager ($275,000) and AI Safety ($274,200). By seniority level: Entry: $97,880; Mid: $165,000; Senior: $227,400; Director: $247,800; VP: $250,000.
Tata Consultancy Services (TCS) AI Hiring
Tata Consultancy Services (TCS) has 27 open AI roles right now. They're hiring across AI/ML Engineer, AI Consultant, Data Scientist, AI Architect. Positions span Sunrise, FL, US, Atlanta, GA, US, Austin, TX, US. Compensation range: $90K - $210K.
Location Context
Across all AI roles, 15% (590 positions) offer remote work, while 3,217 require on-site attendance. Top AI hiring metros: New York (2,643 roles, $211,000 median); San Francisco (2,168 roles, $253,000 median); Los Angeles (1,792 roles, $191,580 median).
Career Path
Common paths into AI Consultant roles include Software Engineer, Data Scientist, Data Analyst.
From here, career progression typically leads toward Senior Engineer, AI Architect, Engineering Manager, Principal Engineer.
Focus on building things that work. A deployed project that solves a real problem is worth more than any certification. Contribute to open-source, build portfolio projects, and invest in fundamentals (software engineering, statistics, systems design) rather than chasing the latest framework. The AI field moves fast, but the engineers who succeed long-term are the ones with strong fundamentals who can adapt to new tools and paradigms as they emerge.
What to Expect in Interviews
AI interviews typically combine coding challenges (Python-focused), system design questions tailored to the role, and discussions about your experience with relevant tools and frameworks. Strong candidates demonstrate both technical depth and the ability to make pragmatic engineering tradeoffs. Prepare portfolio projects that demonstrate end-to-end capability rather than isolated skills.
When evaluating opportunities: Look for job postings that specify the problems you'll work on, the tech stack, and the team structure. Vague postings that list every AI buzzword are often a sign the company hasn't figured out what they need. Strong postings describe the product context, the team you'd join, and the specific challenges you'd tackle.
AI Hiring Overview
The AI job market has 3,823 open positions tracked in our dataset. By seniority: 112 entry-level, 1,798 mid-level, 1,516 senior, and 397 leadership roles (Director, VP, C-Level). Remote roles make up 15% of the market (590 positions). The remaining 3,217 roles require on-site or hybrid attendance.
The market median for AI roles is $200,100. Top-quartile compensation starts at $253,500. The 90th percentile reaches $307,500. Highest-paying categories: AI Engineering Manager ($275,000 median, 41 roles); AI Safety ($274,200 median, 55 roles); Research Engineer ($260,000 median, 434 roles).
AI hiring keeps growing across industries. Companies in tech, finance, healthcare, and retail are all building AI teams. The strongest demand is for people who can bridge the gap between AI research and production engineering. The shift toward generative AI has created new role types (LLM Engineer, Prompt Engineer, AI Agent Developer) that didn't exist three years ago, while traditional roles (Data Scientist, ML Engineer) have evolved to incorporate LLM capabilities.
The AI Job Market Today
The AI job market spans 3,823 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (2,629), Data Scientist (322), AI Software Engineer (279). These three account for the majority of open positions, though smaller categories often have higher per-role compensation because of specialized skill requirements.
The seniority mix tells a story about where AI teams are in their maturity. Entry-level roles (112) are outnumbered by mid-level (1,798) and senior (1,516) positions, reflecting that most companies are past the 'build a team from scratch' phase and need experienced engineers who can ship production systems. Leadership roles (Director, VP, C-Level) total 397 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 15% of all AI roles (590 positions), with 3,217 requiring on-site or hybrid attendance. The remote share has stabilized after the post-pandemic correction. Senior and specialized roles (Research Scientist, ML Architect) are more likely to be remote-eligible than entry-level positions, partly because experienced hires have more negotiating power and partly because these roles require less hands-on mentorship.
AI compensation is structured in clear tiers. The market median sits at $200,100. Top-quartile roles start at $253,500, and the 90th percentile reaches $307,500. These figures include base salary with disclosed compensation. Total compensation (including equity, bonuses, and sign-on) runs 20-40% higher at companies that offer those components.
Category matters for compensation. AI Engineering Manager roles lead at $275,000 median, while Prompt Engineer roles sit at $140,000. The spread between highest and lowest-paying categories reflects the premium on specialized technical skills versus broader analytical roles.
The most in-demand skills across all AI postings: Python (1,979 postings), Aws (1,190 postings), Azure (899 postings), Rag (839 postings), Gcp (726 postings), Pytorch (595 postings), Prompt Engineering (595 postings), Claude (540 postings). Python dominates, appearing in the vast majority of role descriptions regardless of category. Cloud platform experience (AWS, GCP, Azure) is the second most common requirement. The newer entrants to the top skills list (RAG, vector databases, LLM APIs) reflect the shift from traditional ML toward generative AI applications.
Frequently Asked Questions
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