Machine Learning (ML)
Data AnalyticsKey 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.
Shubham Vishwakarma
Full Stack Developer - Averlon
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Follow CutshortShubham Vishwakarma
Full Stack Developer - Averlon