Director - Solution Architect

We need to output HTML fragment with sections only if real content. Use

for headings: About the role, Responsibilities, Requirements, Qualifications, Skills, Benefits, Pay, Schedule. We must not repeat job title as heading. We need to extract content. Given posting: It includes Reports To, Department, Location, then description about company, then video link, then Job Summary (which is essentially About the role). Then sections: Pre-Sale: Client Engagement & Solution Design (list of duties), Post-Sale: Delivery & Client Success (list), Capability Building (list). Then Required Qualifications (list). Then Technical Skills (list). Then Commercial & Leadership Skills (list). Then Why This Role (list). Then disclaimer etc which we should remove (boilerplate). Also we need to remove cookie/navigation/legal/EEO etc. The disclaimer and note about background verification etc likely considered boilerplate; we should drop. Also social media links etc drop. We need to preserve all substantive information. So we need sections: About the role (Job Summary). Responsibilities (maybe combine Pre-Sale, Post-Sale, Capability Building). Requirements (maybe Required Qualifications). Qualifications (maybe education? but we have Bachelor's degree in related field as part of Required Qualifications). Skills (Technical Skills, Commercial & Leadership Skills). Benefits, Pay, Schedule: not present, so omit those sections. We must not create sections with filler. So only include sections where we have content. We need to use

• for any list of duties/requirements/benefits. Use

  for prose. We need to preserve all substantive info, not invent or summarize away detail. Thus we need to extract each bullet point as

• . The posting uses numbered list for Required Qualifications (12+ years...). Also bullet points for Technical Skills etc. We'll convert each to
• . We need to keep the text exactly as given (preserve). Ensure not to lose any details. Let's parse. Company description paragraph: "Evalueserve is a leading global company that develops innovative and sustainable solutions for a wide range of clients, including more than 30% of the Fortune 500 companies. With presence in more than 45 countries across five continents, we excel in leveraging state-of-the-art technology, domain-specific AI solutions, and deep subject matter expertise to elevate our clients' strategic decision-making and business impact. Our clients include Amazon, Deutsche Bank, Johnson & Johnson, McDonald's, Microsoft, Morgan Stanley, Nestlé, Novo Nordisk, and Procter & Gamble. We have 5,000+ talented professionals operating across 45 countries, including India, China, Chile, Romania, the US, and Canada. Our global network also extends to emerging markets such as Colombia, the Middle East and the rest of Asia-Pacific. Recognized by Great Place to Work in India, Chile, Romania, the US, and the UK, we offer a dynamic, growth-oriented, and open culture that prioritizes flexible work-life balance, diverse and inclusive teams, and equal opportunities for all." Then "Watch this video to understand what it means to Elevate Your Impact at Evalueserve!" This is maybe a call to action; could be considered boilerplate? It's not navigation/legal but it's promotional. Might be considered part of description; we can keep as a sentence? It's not a list; we can keep as

  . However it's a call to action to watch video; might be considered promotional but not disallowed. We'll keep. Job Summary paragraph: "The AI Solution Architect will lead client-facing architecture, presales, and delivery for predictive, optimization, classification, forecasting, and decision-intelligence solutions. This role is suited to someone who can connect business priorities to robust AI/ML systems that are explainable, scalable, and operationally reliable." Then Pre-Sale: Client Engagement & Solution Design: list of duties (multiple sentences). We'll treat each sentence as a bullet? The instruction: Use

  • for any list of duties/requirements/benefits. So we need to make a list. The content includes multiple sentences separated by periods. We can split into items. Let's extract: - Partner with sales to lead technical discovery conversations with enterprise prospects seeking data, analytics, and machine learning solutions. - Translate client challenges into structured classical AI roadmaps covering use-case prioritization, data readiness, model design, deployment, and adoption. - Design and present end-to-end solution architectures for forecasting, optimization, anomaly detection, recommendation, and decision-support use cases. - Develop proof-of-concepts, solution estimates, and technical narratives that help shape pursuit strategy and accelerate deal closure. - Serve as the technical lead in client workshops, answering questions on model performance, explainability, feature engineering, and operationalization. Post-Sale: Delivery & Client Success: - Own the solution through delivery, ensuring the final implementation aligns with the architecture, outcomes, and commercial commitments made during sale. - Lead cross-functional teams across data engineering, data science, cloud, and business stakeholders. - Advise senior client stakeholders on industrializing classical AI in production environments. - Identify follow-on opportunities in analytics modernization, model expansion, MLOps, and decision automation. Capability Building: - Create reusable templates, reference architectures, and delivery playbooks for recurring AI use cases. - Contribute to internal knowledge assets, client case studies, and go-to-market collateral. - Mentor junior architects, data scientists, and analysts on AI solution design and client communication. - Provide feedback to leadership on market demand, delivery lessons, and differentiators. Required Qualifications: List: 1. 12+ years of experience in data, analytics, AI/ML consulting, solution architecture, or enterprise technology delivery. 2. Proven experience with enterprise clients across industries such as financial services, retail, manufacturing, healthcare, or industrials. 3. Background in a client-facing technical role where you have shaped solutions and supported delivery. 4. Demonstrated success guiding projects from technical discovery to proposal, implementation, and handoff. 5. Bachelor’s degree in related field. Technical Skills: - Strong understanding of AI and machine learning methods such as regression, classification, clustering, forecasting, optimization, and anomaly detection. - Experience with data engineering, feature engineering, model deployment, monitoring, and MLOps practices. - Ability to design solutions across cloud platforms, APIs, data platforms, and analytics stacks. - Working knowledge of model governance, validation, interpretability, and enterprise risk controls. Commercial & Leadership Skills: - Excellent executive communication and storytelling skills, with the ability to make technical tradeoffs clear to business leaders. - Strong commercial judgment in scoping, estimation, and solution shaping. - Collaborative mindset and ability to influence across sales, delivery, and technical teams. - Comfortable working independently in ambiguous environments and building structure where needed. Why This Role: - You will shape how enterprise clients adopt AI beyond pilots and into production-scale value. - You will work on high-value use cases where AI drives measurable business outcomes. - You will have the chance to define delivery standards and reusable solution patterns. - You will join a practice with global reach, strong analytics depth, and the ability to scale impact across accounts. Now we need to map to sections. We have About the role: could be Job Summary plus company description? The instruction: Use

    for section headings (e.g. About the role, Responsibilities, Requirements, Qualifications, Skills, Benefits, Pay, Schedule). So we need to decide which headings to use. We have content for About the role (Job Summary). Could also include company description as part of About the role? Probably yes. Responsibilities: combine Pre-Sale, Post-Sale, Capability Building. Requirements: maybe Required Qualifications. Qualifications: maybe education? But we already have Bachelor's degree in Required Qualifications. Could split but we might just have Requirements and Qualifications both? The instruction says we can use those headings if we have real content. We have Required Qualifications (which includes experience, proven experience, background, demonstrated success, Bachelor's degree). That could go under Requirements. Qualifications could be maybe the Bachelor's degree? But that's already included. To avoid duplication, we could put Requirements as the list of qualifications (including education). And maybe Qualifications section could be omitted if no distinct content. Better to follow typical pattern: Requirements = mandatory experience/skills; Qualifications = education/certifications. But we have only Bachelor's degree. Could still create Qualifications section with that bullet. However we must not create sections with filler; if we put only Bachelor's degree, that's substantive. So we can have Qualifications section with that bullet. Skills: we have Technical Skills and Commercial & Leadership Skills. Could combine into Skills section with two sublists? But we need to use

    • for any list. Could have one
      with all skill bullets, but we might want to keep distinction? Not required. We'll just put all skill bullets under Skills section. Thus sections:

      About the role

      ... company description + Job Summary + video line?

      Responsibilities

      • ... each bullet from Pre-Sale, Post-Sale, Capability Building

      Requirements

      • ... Required Qualifications items except Bachelor's degree? Actually we could include all items; but we also have Qualifications separate for Bachelor's degree. Let's split: Requirements: experience items (1-4). Qualifications: Bachelor's degree. Thus Requirements list items 1-4.

        Qualifications

        • Bachelor’s degree in related field.

        Skills

        • Technical Skills bullets
        • Commercial & Leadership Skills bullets
        We should not include Benefits, Pay, Schedule. We must ensure we preserve all substantive info: we omitted the video line? We included in About the role paragraph. Also we omitted the "Watch this video..." line; we included as part of paragraph. Good. We omitted the disclaimer, note about background verification, social media links, privacy policy, etc. Good. We need to ensure we didn't lose any bullet content. Let's craft the About the role paragraph: combine company description and Job Summary and video line. Company description paragraph: as above. Then maybe a space then Job Summary: "The AI Solution Architect will lead client-facing architecture, presales, and delivery for predictive, optimization, classification, forecasting, and decision-intelligence solutions. This role is suited to someone who can connect business priorities to robust AI/ML systems that are explainable, scalable, and operationally reliable." Then video line: "Watch this video to understand what it means to Elevate Your Impact at Evalueserve!" We can combine into one

        with sentences separated by space. Now Responsibilities bullets: need to list all bullets from Pre-Sale, Post-Sale, Capability Building. Let's list them precisely: Pre-Sale: - Partner with sales to lead technical discovery conversations with enterprise prospects seeking data, analytics, and machine learning solutions. - Translate client challenges into structured classical AI roadmaps covering use-case prioritization, data readiness, model design, deployment, and adoption. - Design and present end-to-end solution architectures for forecasting, optimization, anomaly detection, recommendation, and decision-support use cases. - Develop proof-of-concepts, solution estimates, and technical narratives that help shape pursuit strategy and accelerate deal closure. - Serve as the technical lead in client workshops, answering questions on model performance, explainability, feature engineering, and operationalization. Post-Sale: - Own the solution through delivery, ensuring the final implementation aligns with the architecture, outcomes, and commercial commitments made during sale. - Lead cross-functional teams across data engineering, data science, cloud, and business stakeholders. - Advise senior client stakeholders on industrializing classical AI in production environments. - Identify follow-on opportunities in analytics modernization, model expansion, MLOps, and decision automation. Capability Building: - Create reusable templates, reference architectures, and delivery playbooks for recurring AI use cases. - Contribute to internal knowledge assets, client case studies, and go-to-market collateral. - Mentor junior architects, data scientists, and analysts on AI solution