Solution Specialist, Data & AI Practice

• *Role Summary:**

The Solutioning Support/Specialist, is an early/mid\-career role in the Data \& AI Practice with a clear commercial edge. The person here works alongside Senior SMEs and Practice Head to design Data, AI, and GenAI solutions, build credible client proposals, and convert business problems into structured learning and transformation journeys.

A strong Solutioning Specialist/Support will quickly become the person in product owners and pre\-sales reach for during live pursuits — because the inputs they produce are crisp, accurate, and ready to use. Pitch\-decks and proposals this person contributes to are well\-structured and buyer\-specific without significant rework; a current library of program structures and case study inserts exists and gets reused across pursuits; market intelligence on AI / GenAI lands in usable form, not as raw research dumps; and Senior SMEs trust this person with end\-to\-end ownership of mid\-complexity proposals, not just individual sections.

He/she will be in the room for client discussions, contributing to live pursuits, and developing the kind of structured thinking and solutioning craft that scales into senior roles. Curiosity, ownership, and the ability to turn messy inputs into a crisp, buyer\-ready output are what make this role valuable.

• *Key Responsibilities:**

• *1\. Solutioning \& Proposal Development**

• Partner with Senior SMEs and Practice Leads to design end\-to\-end Data \& AI solutions for client pursuits — contributing to structure, content, and commercial framing.
• Build client proposals, solution documents, and program structures that are well\-organized, accurate, and ready to use without significant rework.
• Translate client requirements into structured, outcome\-oriented learning journeys — adoption, capability uplift, and measurable business outcomes, not just module lists.
• Support customized offerings across Data Engineering, AI / ML, and GenAI and Agentic AI tracks; help assemble pursuits from existing accelerators rather than rebuilding from scratch.

• *2\. Client Engagement Support**

• Participate in client discussions, discovery calls, and requirement\-gathering sessions — capture context with the rigour that makes the next conversation sharper.
• Convert business needs into solution frameworks and delivery models with guidance from Senior SMEs; document customer priorities so Practice and Sales can act on them.
• Support pitch decks, case studies, and success stories — buyer\-specific, visually clean, and aligned to how the customer thinks about their own problem.
• Stay engaged through the proposal cycle and handoff to delivery; ensure no requirement gets lost between discovery and execution.

• *3\. Content \& Program Structuring**

• Assist in designing curriculum outlines, learning journeys, and hands\-on lab structures that hold up against real\-world enterprise contexts.
• Work with internal and external SMEs to ensure content aligns with current industry trends, real use cases, and business outcomes — not generic technology overviews.
• Maintain a library of reusable program structures, slide assets, and case study inserts; flag gaps in the existing content library proactively.

• *4\. Research \& Market Intelligence**

• Track trends across the AI / GenAI / LLM ecosystem and Data Engineering \& Analytics — translate findings into usable inputs for outreach, pitching, and offering design.
• Identify new solution opportunities and product ideas based on market signals, customer asks, and competitor moves.
• Benchmark StackRoute's offerings against competitors; surface gaps and differentiation angles for Senior SMEs and Practice Leads to act on.

• *5\. Internal Collaboration**

• Work fluidly with Delivery, Sales, and external SMEs to ensure solutions designed on paper actually work in delivery — surface feasibility risks early, not late.
• Coordinate inputs across Practice teams during pursuit cycles; hand off to delivery with documentation that captures customer commitments and success metrics.

• *Must Have Technical \& Functional Skills:**

• Good understanding of terminologies in Data Engineering (ETL, pipelines, data lakes), Data Analytics \& BI concepts, and Machine Learning fundamentals, AI tools ( not technical expertise but L1\-L2 knowledge should be present from application standpoint).
• Awareness of GenAI / LLM vocabulary — prompt engineering, RAG, APIs — with enough depth to hold a credible first conversation with a technical stakeholder.
• Strong PowerPoint skills (client\-ready decks), Excel for effort estimation and costing basics, and structured documentation — proposals, SoWs, one\-pagers.
• Strong problem\-solving and structured thinking — breaks complex requirements into clear, communicable solutions.
• Comfortable communicating with both technical and non\-technical stakeholders; good storytelling and presentation instincts; understanding of L\&D context is a plus.

• *Qualifications \& Experience:**

• *Required:**

• 5\-15 years in the education products, or in solutioning, pre\-sales, or consulting roles with exposure of 3\-5 years in Data/AI/Analytics.
• Bachelor's or Master's in Computer Science, Data Science, Engineering, or a related discipline.

• *Nice to Have competences:**

• Prior pre\-sales, proposal writing, or design development experience.
• Certifications in cloud, data, or AI platforms (AWS, Azure, GCP, or model\-provider certifications).

• *Core Competencies:**

• **Structured Thinking:** Organises ambiguous client and technical inputs into logical, buyer\-relevant narratives. Builds proposals that flow from problem to solution.
• **Solution Articulation:** Translates Data \& AI capabilities into crisp, persona\-specific stories. Adapts the pitch for a CTO, L\&D Head, or BU Head without losing substance.
• **Research \& Synthesis:** Gathers and distils large volumes of information into sharp, usable outputs. Knows what to include and what to leave out.
• **Written Communication:** Writes a tight brief, a clean slide, and a clear email. Adapts register from internal working notes to buyer\-facing collateral.
• **Curiosity \& Learning Agility:** Picks up new tools, concepts, and sectors quickly. Tracks AI / GenAI shifts proactively rather than waiting to be told what to read.
• **Bias for Action:** Ships a useful 10\-slide deck on time rather than a polished 20\-slide deck that's late. Comfortable with iteration over perfection.

Pay: ₹2,000,000\.00 \- ₹2,100,000\.00 per year

Benefits

• Health insurance
• Leave encashment
• Life insurance
• Paid sick time
• Paid time off
• Provident Fund

Application Question(s)

• Do you have minimum 3 years of experience in solutioning, pre\-sales, or consulting specifically involving Data, Analytics, or AI/GenAI solutions?
• Which Data \& AI concepts have you actively incorporated into client proposals, learning journeys, or solution frameworks?

Work Location: In person