The Opportunity – Build, Lead & Architect the Future of Enterprise AI

We are looking for a Founding AI Forward Deployed Engineering (FDE) Lead / Architect to take ownership of Anthrobyte's end-to-end AI technology function.

This is not a conventional AI leadership position focused solely on strategy, team management, or architecture reviews. It is a high-impact, hands-on leadership opportunity for someone who can move seamlessly between enterprise client conversations, AI solution architecture, hands-on engineering, production deployment, and technology leadership.

You will work directly with the CEO and enterprise stakeholders, translating complex business challenges into scalable, production-ready AI solutions.

From identifying high-value AI opportunities and architecting intelligent systems to building engineering teams, establishing project pods, and driving successful enterprise deployments, you will own the complete AI solution lifecycle.

Your mandate will be to build the technology function, establish architectural excellence, lead client-facing AI engagements, and evolve Anthrobyte's engineering capabilities as the company scales.

Key Responsibilities1. Enterprise AI Consulting & Strategic Client Engagement

- Lead enterprise and mid-market client engagements across global markets, driving technical discovery and AI transformation conversations.
- Engage directly with CXOs, CTOs, CIOs, VPs of Operations, and technology decision-makers to understand business challenges, operational bottlenecks, and AI opportunities.
- Conduct AI readiness assessments, technical workshops, stakeholder interviews, and value-stream mapping exercises.
- Translate ambiguous business requirements into clearly defined AI use cases, technical roadmaps, and measurable business outcomes.
- Establish yourself as a trusted AI advisor, building strategic client relationships that drive long-term partnerships and business expansion.

2. Pre-Sales, Solution Consulting & Business Enablement

- Own technical pre-sales engagements, solution discovery, opportunity qualification, and AI solution positioning.
- Develop compelling Proof of Concepts (PoCs), technical demonstrations, architecture blueprints, and enterprise solution proposals.
- Lead technical responses to RFPs, RFIs, and complex enterprise solution requirements.
- Define solution scope, implementation feasibility, data requirements, resource estimates, delivery timelines, and expected ROI.
- Partner with the commercial and leadership teams to convert enterprise opportunities into successful AI engagements.

3. AI Architecture & Hands-on Engineering Leadership

- Design and architect scalable, secure, production-grade AI/ML systems for complex enterprise environments.
- Lead the development of advanced AI capabilities, including:
- Large Language Models (LLMs) and Small Language Models (SLMs).
- Retrieval-Augmented Generation (RAG) architectures.
- Agentic AI systems and multi-agent orchestration.
- Multimodal AI solutions.
- NLP and Computer Vision applications.
- LLM orchestration, intelligent automation, and workflow agents.
- Enterprise AI integrations and advanced analytics.
- Build and optimize AI pipelines using Python and frameworks such as LangChain, LlamaIndex, Hugging Face, and other emerging technologies.
- Drive cloud-native AI architecture and deployment across AWS, Azure, and GCP.
- Establish best practices for model evaluation, prompt engineering, observability, MLOps, model monitoring, security, and governance.
- Optimize inference costs, token consumption, cloud credits, model performance, latency, and infrastructure utilization.
- Remain technically hands-on and capable of debugging, prototyping, and solving complex engineering challenges.

4. End-to-End Delivery & Production Ownership

- Own the complete AI solution lifecycle, from initial discovery and architecture through development, deployment, and continuous optimization.
- Ensure seamless integration with enterprise systems, databases, APIs, and existing business workflows.
- Drive production readiness, deployment planning, scalability, reliability, and performance optimization.




- Oversee go-live milestones, client acceptance, and post-deployment performance.
- Establish measurable success metrics and ensure solutions deliver tangible business value.
- Take complete accountability for production issues, technical challenges, and solution improvements.

5. Technology Function & Engineering Team Leadership

- Build and develop Anthrobyte's AI engineering and technology function from the ground up.
- Establish a scalable engineering organization with clearly defined ownership, responsibilities, and delivery standards.
- Create dedicated project-based engineering pods, aligning AI engineers, data scientists, backend engineers, and consultants with individual client engagements.
- Recruit, mentor, and develop high-performing engineering teams.
- Establish engineering standards, architectural governance, technical documentation, and development methodologies.
- Build reusable frameworks, accelerators, and solution components to improve delivery efficiency.
- Develop a culture of technical ownership, experimentation, collaboration, and execution excellence.

6. Technology Innovation & Product Evolution

- Define and continuously evolve Anthrobyte's enterprise AI architecture strategy.
- Identify emerging AI technologies, frameworks, research, and industry developments that can create a competitive advantage.
- Translate relevant innovations into practical enterprise solutions and reusable product capabilities.
- Work closely with the leadership team to evolve the company's services-plus-product business model.
- Contribute to the product roadmap, technology vision, and long-term engineering strategy.
- Develop technical case studies, internal playbooks, solution accelerators, and architecture standards.

Ideal Candidate Profile:-

We are looking for a technology leader who combines deep AI engineering expertise, enterprise consulting capabilities, commercial awareness, and the entrepreneurial mindset required to build a technology organization from zero to one.

Mandatory Experience & Leadership Requirements

1. AI/ML Engineering Experience

- 7+ years of overall professional experience, with substantial hands-on experience delivering production-grade AI/ML solutions.
- Strong expertise in LLMs, NLP, Computer Vision, advanced analytics, or enterprise AI applications.
- Proven experience building and deploying sophisticated AI systems rather than working exclusively on research or theoretical implementations.

2. Advanced AI Architecture & Engineering

- Demonstrated expertise in designing and implementing RAG pipelines, SLMs, agentic AI systems, multi-agent workflows, LLM orchestration, and multimodal architectures.
- Ability to make hands-on technical decisions involving model selection, fine-tuning, evaluation, inference optimization, and deployment.
- Strong understanding of AI infrastructure, scalability, performance, cost optimization, and production constraints.

3. Enterprise Client-Facing & Consulting Experience

- At least 4 years of experience in client-facing technical consulting, enterprise solution architecture, pre-sales, or AI solution delivery.
- Proven experience engaging with CXOs, VPs, business leaders, and enterprise technology stakeholders.
- Strong track record in technical discovery, solution design, business case development, PoC demonstrations, and enterprise proposals.

4. AI Solution Delivery & Implementation

- Experience owning end-to-end enterprise AI implementations, including data ingestion, pipelines, model development, integration, deployment, monitoring, and optimization.
- Strong understanding of production environments, enterprise integrations, deployment challenges, and change management.
- Demonstrated accountability for successful go-live,



client acceptance, and post-deployment outcomes.

5. Team Building & Technology Leadership

- Proven experience building, managing, and mentoring AI/ML engineering teams.
- Ability to establish project-based engineering pods and manage multiple client delivery engagements.
- Experience defining technical standards, architecture patterns, delivery processes, and engineering practices.
- Capable of balancing strategic leadership with hands-on technical execution.

6. Technical Skills

- Programming: Python.
- AI/ML: LLMs, SLMs, NLP, Computer Vision, Deep Learning.
- Frameworks: LangChain, LlamaIndex, Hugging Face.
- AI Architecture: RAG, Agentic AI, Multi-Agent Systems, Multimodal AI, LLM Orchestration.
- Cloud Platforms: AWS, Microsoft Azure, Google Cloud Platform.
- Engineering: API Integrations, MLOps, Model Deployment, Monitoring, AI Optimization.

7. Business & Communication Skills

- Exceptional communication, presentation, and stakeholder management capabilities.
- Ability to explain complex AI architectures and technical concepts to non-technical business stakeholders.
- Strong business acumen with an understanding of enterprise ROI, commercial feasibility, and solution scalability.
- Comfortable navigating ambiguity and taking ownership in a fast-moving startup environment.

Preferred Industry & Background

- Experience working with AI/ML services companies, enterprise technology firms, data analytics organizations, or AI-focused product companies.
- Exposure to both services-led consulting and product development environments.
- Experience in early-stage startups or high-growth organizations, particularly in building AI solutions from the ground up.
- Supply Chain, Manufacturing, Logistics, B2B SaaS, Enterprise AI, or MLOps domain exposure will be a strong advantage.
- Experience serving international clients, particularly across the US, Europe, Middle East, or Southeast Asia.

Educational Background:

- Bachelor's or Master's degree in Computer Science, Information Technology, or a closely related technical discipline.
- Graduates from IITs, BITS, NITs, IIITs, or other reputed institutions are preferred.
- Strong technical expertise and demonstrated delivery capabilities will also be considered.

Pay: ₹6,000,000.00 - ₹9,000,000.00 per year

Application Question(s):

- How many years of overall skilled experience do you have, including at least 7 years in AI/ML, software engineering, or related technology domains?
- Do you have a minimum of 4 years of hands-on experience in enterprise client-facing roles, AI consulting, solution architecture, or technical pre-sales, including CXO-level stakeholder engagement?
- Have you independently architected and deployed production-grade AI solutions involving LLMs, RAG pipelines, SLMs, Agentic AI, or multi-agent workflows?
- Are you proficient in Python, LangChain, LlamaIndex, Hugging Face, and at least one major cloud platform (AWS, Azure, or GCP), with practical implementation experience?
- Have you owned end-to-end enterprise AI engagements, including technical discovery, business use-case identification, solution architecture, PoC development, RFP/RFI responses, and production deployment?
- Have you independently managed production AI systems, including enterprise integrations, MLOps, monitoring, model optimization, inference cost reduction, and troubleshooting?
- Have you built and led AI/ML engineering teams, established project-based delivery pods, and mentored engineers while remaining technically hands-on?
- Do you have professional experience across both AI/ML services or consulting and AI product development environments, preferably in an early-stage or high-growth organization?
- Are you willing to work from the office five days a week at Kokapet, Hyderabad, and initially join on a consultant basis with the possibility of transitioning to full-time employment?
- What are your current CTC, expected CTC, notice period, and earliest possible joining date?

Education:

- Bachelor's (Required)

Experience:

- hands-on production AI/ML work : 7 years (Required)

Work Location: In person

📌 Founding AI FDE Lead / Architect – Enterprise AI Solutions (Hyderabad)
🏢 Talent Aquisition Consultant
📍 Hyderabad