AI Tech Lead

Title - AI Architect

Experience: 8–12 years

Location: Hyderabad (Hybrid)

Role Summary:

We P99Soft are looking for a senior AI Architect to own system-level design and architecture decisions across a portfolio of production AI initiatives. This role goes beyond assembling common frameworks - it requires the judgment to evaluate deep architectural trade-offs (model quality vs. size vs. cost, deterministic vs. generative approaches, and verification/testing strategy) and translate them into coherent, maintainable system architecture at scale.

Key Responsibilities:

• Define end-to-end system architecture and technical direction for AI-driven platforms, spanning retrieval-augmented generation, agentic/tool-using systems, vision and language model applications, and semantic search.
• Evaluate and own core architectural trade-offs: model quality vs. size vs. cost, deterministic vs. generative system design, latency and scalability, and the design of output-verification and evaluation harnesses.
• Provide technical leadership and design review across multiple concurrent initiatives, guiding implementation teams on build-out.
• Track and apply emerging AI/ML research and techniques where they meaningfully improve system design and reliability.
• Define the boundary between deterministic, verifiable system components and generative/agentic components based on the problem being solved.
• Serve as the technical escalation point for complex system-level issues — reliability, performance, or output-quality problems beyond routine debugging.

Required Experience & Skills:

• 8–12 years of overall engineering experience, including substantial hands-on depth in applied or foundational AI/ML system design - this is a non-negotiable baseline, not familiarity with AI tools as an end user.
• Demonstrated experience architecting AI/ML systems at production scale, beyond simple retrieval or prompt-based implementations.
• Strong grasp of trade-offs across model quality, size, cost, latency, and determinism, and how these decisions shape system architecture.
• Working knowledge of current and emerging generative AI / LLM techniques and research directions, and the ability to evaluate when they are appropriate to apply.
• Experience architecting one or more of: retrieval-augmented generation pipelines, agentic/tool-using systems, vision models, semantic search, or automated verification and evaluation systems.
• Comfort operating across multiple concurrent initiatives and translating research-adjacent concepts into buildable, production-grade systems.

Nice to Have:

• Experience in domains with strong internal-tooling or quality/verification requirements.
• Exposure to developer-facing tooling or code-analysis systems.

Benefits

• Competitive salary and performance-based bonuses.
• Comprehensive health insurance.
• Paid time off and flexible working hours.
• Professional development opportunities and support for continuing education.
• Udemy Learning Licenses to enhance skills and stay up-to-date with the latest technology.

P99Soft is an Equal-Opportunity Employer

P99Soft is an equal-opportunity employer. At P99soft, we are committed to providing equal employment opportunities regardless of job history, disability, gender identity, religion, race, color, caste, marital/parental status, veteran status, or any other special status. We stand against the discrimination of employees and individuals and are proud to be an equitable workplace that welcomes individuals from all walks of life if they fit the designated roles and responsibilities.

Life @ P99Soft

At P99soft, we believe in creating a work environment that balances professional excellence with personal well-being. Our vibrant office culture is built on mutual respect, inclusivity, and a shared passion for innovation. We host regular team-building activities, workshops, and social events to foster a sense of community and collaboration among our employees. Join us and be part of a team that values your contributions and supports your growth.

About Us

P99soft is a leading IT Services and IT Consulting company dedicated to making our customers' business operations seamless by providing them with digital services and applications using the latest technologies. We are committed to bringing excellence to all our creations. We need passion, empathy, and being customer-focused to achieve technical excellence. We believe in transparency through open communication and work with honesty and integrity while dealing with our customers and partners.

Our Vision: “To lead digital transformation for our customers, delighting them at every step, while empowering our employees and fostering success.”

Our Mission: “To attain our objectives within a framework of integrity, transparency, and respect for our clients, employees, partners, and the community, fostering mutual trust and sustainable growth.”

Journey towards digital transformations begins here. Our talented teams help you make informed decisions, enhance operation efficiencies, create state-of-the-art visual interfaces, and design dynamic workflows to bring a better customer experience. Our engineering teams take complete ownership of assigning the right talent and architecting solutions using the latest technologies.

AI Architect – Enterprise AI & Generative AI ( Remote) - Contractual

Experience: 8+ years

Location: Remote, India

Employment Type: Contractual

Working Hours: 3:00 PM – 12:00 AM IST

Client Engagement: US-based client

About the Role

We are looking for a hands-on, client-facing AI Architect to lead the architecture, technical direction, and production implementation of enterprise AI and Generative AI solutions for a US-based client.

You should combine strong software engineering and cloud architecture fundamentals with practical experience building secure, scalable, and reliable AI applications. This role involves end-to-end technical ownership, from solution design and prototyping to production deployment and optimization. It is not a research-only, advisory-only, or prompt-writing position.

Key Responsibilities

• Lead end-to-end architecture and delivery of AI/ML and Generative AI solutions, from discovery and proof of concept to production rollout.
• Design LLM-powered applications using Retrieval-Augmented Generation (RAG), embeddings, vector databases, AI agents, and orchestration frameworks.
• Define data ingestion, chunking, retrieval, grounding, evaluation, and knowledge-refresh strategies.
• Select appropriate AI models and platforms based on quality, latency, cost, security, and operational requirements.
• Design cloud-native architectures, APIs, integrations, identity controls, monitoring, and resilient deployment patterns.
• Create architecture diagrams, technical designs, implementation roadmaps, and technical standards.
• Implement AI quality and safety measures, including hallucination mitigation, guardrails, access controls, and prompt-injection defenses.
• Guide engineering, data, ML, and DevOps teams through implementation, code reviews, troubleshooting, and go-live readiness.
• Lead technical discussions and design reviews with US client stakeholders.
• Establish production monitoring, CI/CD, versioning, incident handling, and performance and cost optimization practices.

Required Skills & Qualifications

• 8+ years of relevant experience in software engineering, AI/ML engineering, data engineering, or technology architecture.
• Proven experience personally architecting and deploying at least one enterprise-grade AI or Generative AI solution into production.
• Strong practical knowledge of LLMs, RAG, embeddings, vector databases, orchestration frameworks, and model evaluation.
• Hands-on experience with Python and APIs, including prototyping, code review, and troubleshooting.
• Experience with at least one major cloud platform: Microsoft Azure, AWS, or Google Cloud Platform (GCP).
• Understanding of data pipelines, SQL/NoSQL databases, containers, CI/CD, MLOps, and LLMOps.
• Knowledge of enterprise security, privacy, identity and access management, and responsible AI practices.
• Experience independently leading architecture workshops, design reviews, and technical discussions with US clients.
• Excellent written and spoken English, documentation, and stakeholder management skills.
• Availability to work from 3:00 PM to 12:00 AM IST and willingness to accept a contractual engagement.

Preferred Experience

• Azure AI Foundry, Azure OpenAI, Amazon Bedrock, or Google Vertex AI.
• Azure AI Search or equivalent retrieval tools.
• LangChain, LangGraph, Semantic Kernel, and agent/tool orchestration.
• Model fine-tuning, predictive ML, document intelligence, multimodal AI, or real-time AI applications.
• Experience in cloud modernization, enterprise integrations, data analytics, or technology consulting.
• Relevant AI/cloud architecture certifications and experience mentoring technical teams.

What Success Looks Like

• Designing secure, scalable, production-ready AI solutions rather than demos or conceptual presentations.
• Making sound technical decisions around feasibility, performance, reliability, cost, and AI risks.
• Taking ownership of architecture decisions and resolving technical blockers with limited supervision.
• Building trust with senior US stakeholders and collaborating effectively with India-based delivery teams.

Contract Details

• Initial engagement: Minimum 3 months, with extension subject to project requirements and performance.
• Working schedule: 3:00 PM – 12:00 AM IST.
• Potential permanent employment: After 18 months, the client may consider direct permanent employment at its sole discretion. Conversion is not guaranteed.

Mandatory Screening Criteria

Candidates should be able to:

• Explain an enterprise AI/GenAI solution they personally architected and deployed, including architecture, scale, evaluation, and business impact.
• Whiteboard an enterprise RAG or agentic solution covering data flow, security, authentication, authorization, monitoring, and cost controls.
• Discuss real-world model-quality issues, hallucinations, privacy risks, incidents, and architectural trade-offs.
• Lead a technical design review with US client stakeholders.
• Confirm acceptance of the working hours and contractual engagement.

Apply only if you have hands-on experience architecting and deploying enterprise AI/GenAI solutions in production.

Looking for an Architect with strong experience in AI and Data Engineering, preferably with hands-on experience in Generative AI (GenAI). Candidates with experience in any cloud platform (AWS, Azure, or GCP) will be considered, 

Job Title : Head of AI / Forward Deployed AI Architect

Experience : 7+ Years

Job Location : Kokapet, Hyderabad

Working Model : Work From Office (5 Days a Week)

Working Days : Monday to Friday

Work Timings : 09:00 AM to 05:00 PM / 09:00 AM – 06:00 PM

Notice Period : Maximum 60 Days

Reporting To : CEO

About the Role :

We are looking for a highly experienced, hands-on Head of AI / Forward Deployed AI Architect who can lead the company's AI technology function and drive end-to-end AI solution delivery, from client discovery and solution architecture to development, deployment, and production success.

This is a strategic yet deeply technical and client-facing leadership position. The ideal candidate should have strong experience in AI/ML engineering, LLMs, Generative AI, agentic workflows, RAG architectures, and enterprise AI deployments, along with proven expertise in presales, solution consulting, and managing enterprise client relationships.

The person will work directly with the CEO, lead the technical department, establish project-based engineering pods, mentor AI engineers and consultants, and contribute to building the company's long-term AI architecture and technology strategy.

The company operates on a Services-to-Product (Services as Software) model, combining enterprise AI consulting, implementation, and reusable AI-powered solutions.

Mandatory Skills :

Python, Generative AI, LLMs, SLMs, RAG, Agentic AI, LangChain, LlamaIndex, Hugging Face, NLP, AI/ML, Multimodal AI, AWS / Azure / GCP, AI Solution Architecture, Production AI Deployment, Technical Presales, Enterprise Client Management, Team Leadership.

Key Responsibilities :

1. Enterprise Client Engagement & AI Consulting :

• Lead technical discovery sessions with enterprise and mid-market clients across global markets.
• Understand business challenges, operational pain points, data maturity, and AI readiness.
• Engage with CXOs, VPs of Operations, IT leaders, and other senior stakeholders as a trusted AI advisor.
• Conduct AI readiness assessments, technical workshops, and value-stream-mapping sessions.
• Translate ambiguous business requirements into practical AI use cases and implementation roadmaps.
• Build long-term client relationships that generate repeat business, referrals, and expanded engagements.

2. AI Solution Architecture & Presales :

• Own end-to-end solution design for AI consulting proposals, including use-case identification, technical architecture, data requirements, and ROI estimation.
• Develop and present technical PoCs, solution blueprints, architecture diagrams, and proposals.
• Work closely with commercial teams to create technically strong and commercially viable proposals.
• Lead technical responses to RFPs and RFIs.
• Define AI architecture patterns covering LLM orchestration, RAG pipelines, agentic AI workflows, and multimodal AI systems.
• Evaluate emerging AI frameworks, technologies, and research to identify opportunities for business adoption.

3. Hands-on AI Engineering & Deployment :

• Design, develop, and deploy production-grade AI/ML solutions using Python and modern AI frameworks.
• Build and optimize LLM applications, Small Language Models (SLMs), RAG pipelines, agentic systems, and multimodal solutions.
• Work on the complete AI lifecycle, including data ingestion, preprocessing, model development, orchestration, deployment, monitoring, and optimization.
• Handle production constraints, system integrations, scalability, performance, latency, and AI infrastructure costs.
• Optimize LLM token consumption, API credits, model usage, and overall solution economics.
• Troubleshoot production issues, improve system performance, and ensure successful client deployments.
• Drive go-live milestones, client acceptance, and post-deployment success metrics.

4. Technical Leadership & Team Development :

• Lead and develop the company's overall AI and technology department.
• Build a scalable project-based engineering pod structure for different client engagements.
• Define team structures, technical ownership, delivery responsibilities, and engineering standards.
• Recruit, mentor, and develop AI engineers, data scientists, and technical consultants.
• Guide teams on AI architecture, engineering best practices, client communication, and production delivery.
• Collaborate with engineering and data science teams to ensure technical feasibility and high-quality implementation.
• Establish reusable architecture patterns, internal frameworks, and solution accelerators.

5. Technology Strategy & Innovation :

• Define the company's long-term AI technology roadmap and architecture standards.
• Drive innovation across Generative AI, LLM applications, agentic workflows, RAG, and multimodal AI.
• Identify emerging technologies and frameworks that can improve solution delivery and business outcomes.
• Contribute to internal knowledge development through technical documentation, case studies, whitepapers, playbooks, and reusable AI accelerators.
• Help evolve the company's services business towards scalable, repeatable, product-oriented AI solutions.

Mandatory Skills & Experience :

1. Overall Experience :

• 7+ years of overall professional experience.
• Strong hands-on experience in AI/ML, Generative AI, NLP, Computer Vision, or advanced analytics.
• At least 4 years of experience in client-facing AI consulting, presales, solution architecture, or technical advisory roles.
• Proven experience working directly with enterprise-level clients.

2. Core AI & Technical Expertise :

• Strong programming experience in Python.
• Hands-on experience with LLMs, SLMs, and Generative AI applications.
• Experience building and deploying :
• RAG (Retrieval-Augmented Generation) pipelines.
• Agentic AI architectures and multi-agent workflows.
• LLM orchestration and application frameworks.
• Multimodal AI systems.
• NLP and advanced AI/ML solutions.
• Experience with frameworks such as LangChain, LlamaIndex, Hugging Face, or equivalent.
• Hands-on exposure to AWS, Azure, or GCP AI/ML services.
• Understanding of AI model deployment, monitoring, scalability, and production optimization.
• Strong understanding of API integrations, data pipelines, and enterprise system integration.
• Knowledge of LLM cost optimization, token usage, inference efficiency, and API credit management.

3. Presales & Solution Consulting :

• Proven experience in technical presales and AI solution consulting.
• Ability to lead technical discovery, use-case scoping, solution design, and ROI framing.
• Experience creating PoCs, technical demonstrations, architecture blueprints, and proposals.
• Experience responding to enterprise RFPs and RFIs.
• Ability to conduct workshops and AI readiness assessments.
• Strong communication and presentation skills, with the ability to explain complex AI concepts to business stakeholders.

4. Leadership & Client Management :

• Experience building and leading technical teams.
• Ability to establish project-based engineering pods and manage multiple delivery teams.
• Experience mentoring AI engineers, data scientists, and technical consultants.
• Strong ownership of project delivery, deployment, client acceptance, and business outcomes.
• Ability to manage ambiguity and work in a fast-paced startup environment.

5. Business & Industry Background :

• Experience working across both IT services/consulting and product-based environments.
• Preferably from AI/ML companies, data analytics firms, AI consulting organizations, data-platform companies, or B2B SaaS organizations.
• Experience in startup or growing organizations with 0-to-1 AI product or solution development exposure.
• Supply Chain AI, Manufacturing, Logistics, B2B SaaS AI, or MLOps experience will be an added advantage.

6. Educational Background :

• Bachelor's or Master's degree in Computer Science, Information Technology, or a related technical discipline.
• Preferred institutions: IITs, BITS, NITs, and IIITs.
• Strong equivalent academic and professional credentials will also be considered.

Preferred Qualifications :

• Experience handling international clients across the US, Europe, Middle East, or Southeast Asia.
• Strong cross-cultural communication and stakeholder management skills.
• Experience in enterprise AI transformation and large-scale AI implementation.
• Exposure to Supply Chain, Manufacturing, Logistics, or Industrial AI use cases.
• Experience building AI teams and technical functions from an early-stage startup environment.

Ideal Candidate Profile :

The ideal candidate will be a hands-on AI technology leader with strong enterprise consulting and presales capabilities, who can independently own the complete AI engagement lifecycle.

The candidate should :

• Be equally comfortable discussing AI strategy with a CXO and building an AI solution hands-on.
• Have successfully taken AI solutions from initial client discovery to production deployment.
• Demonstrate strong technical depth rather than only high-level architecture or managerial experience.
• Have experience building and scaling technical teams.
• Be comfortable working in ambiguous, fast-moving startup environments.
• Demonstrate strong ownership, business understanding, communication, and problem-solving abilities.
• Be interested in a long-term technical leadership opportunity with equity participation.

Additional Information :

• This is a replacement position for the former AI Head / CTO.
• The company is an approximately one-year-old, self-funded startup with around 10 employees and positive operating performance.
• The position reports directly to the CEO.
• The organization follows a hybrid services-to-product business strategy, moving towards a Services-as-a-Software model.
• Five-day working week.
• Work timings : 09:00 AM to 05:00 PM or 09:00 AM to 06:00 PM.
• Work Location : Kokapet, Hyderabad.
• Candidates from any location across India can apply, provided they are open to relocating to Hyderabad.

Interview Process :

1. Initial Discussion : First-round discussion with the Founder or CFO to understand the candidate's background, experience, leadership capabilities, and expectations.
2. Technical Assessment : One or two technical rounds involving live scenarios to assess practical AI architecture, problem-solving, and hands-on technical expertise.
3. Final Interview : Mandatory face-to-face interview at the Hyderabad office.

Job Details

Position Title: AI Evangelist

Role Purpose

We are seeking a highly experienced AI Evangelist & Core AI Engineer who combines deep technical expertise in Artificial Intelligence with the ability to influence, educate, demonstrate, and accelerate AI adoption across the enterprise.

The role requires deep understanding of AI, Machine Learning, Deep Learning, Generative AI, Large Language Models, Agentic AI, AI engineering, and modern software development practices, combined with significant hands-on experience implementing scalable AI applications in complex enterprise environments.

Key Responsibilities

1. Core AI Engineering & Architecture

• Design, develop, and implement enterprise-grade AI and Generative AI applications.
• Build scalable applications using LLMs, multimodal models, AI agents, RAG, vector databases, knowledge graphs, APIs, and enterprise data platforms.
• Architect AI solutions capable of supporting enterprise-scale workloads with appropriate considerations for:
• Scalability
• Reliability
• Security
• Performance
• Observability
• Cost optimization
• Governance
• Develop and implement advanced Retrieval-Augmented Generation (RAG) architectures including hybrid search, reranking, metadata filtering, semantic retrieval, and knowledge grounding.
• Build multi-agent and agentic AI applications capable of reasoning, tool usage, workflow execution, orchestration, and enterprise system integration.
• Design AI applications integrating structured and unstructured enterprise information.
• Evaluate architecture choices between commercial models, open-source models, small language models, domain-specific models, and hosted AI platforms.
• Implement model routing, prompt orchestration, caching, guardrails, evaluation pipelines, and fallback strategies.
• Work closely with cloud, platform, security, enterprise architecture, DevOps, and application engineering teams.

2. Deep AI / ML / DL Expertise

The candidate should have strong conceptual and practical understanding across the modern AI technology landscape, including:

• Artificial Intelligence /Machine Learning/ Deep Learning/ Neural Networks /LLM/SLM/RAG/Graph RAG / MLOps / LLMOps / GenAIOps

3. Generative AI & LLM Engineering

• Build production-grade applications utilizing leading commercial and open-source LLM ecosystems.
• Design scalable LLM platforms supporting multiple enterprise applications and use cases.
• Develop reusable LLM services, APIs, components, agent frameworks, and reference architectures.

4. AI Agents & Automation

• Design and implement AI agents and agentic workflows that automate complex business and engineering processes.
• Build agents capable of interacting with enterprise applications, APIs, databases, documents, development environments, and workflow platforms.
• Evaluate emerging agent architectures and orchestration frameworks pragmatically.
• Develop reusable patterns for:
• Single-agent systems
• Multi-agent systems
• Human-in-the-loop workflows
• Autonomous workflows
• Tool-calling agents
• Coding agents
• Enterprise knowledge agents
• Establish appropriate controls for AI agent identity, permissions, auditability, security, and human oversight.

5. AI-Enabled Software Engineering

Drive adoption of AI throughout the Software Development Life Cycle.

Chief Engineer - Artificial Intelligence

AI Architecture | Production Engineering | LLM, RAG & Agentic AI

Primary objective: Build secure, scalable and reusable AI capabilities that move from experimentation to reliable production use across DocTutorials products and business functions.

Department

Technology & Product Engineering

Location

Hyderabad / Chennai

Reports to

Vice President - Technology

Experience

10+ years in software engineering, including 5+ years in AI/ML

Role Summary

The Chief Engineer - AI will define and lead AI engineering architecture, standards and production implementation. This is a senior, hands-on technical leadership role responsible for building reliable AI platforms and guiding AI engineers, data teams and application teams.

Key Responsibilities

1. AI Strategy and Architecture

·       Define the AI engineering strategy, architecture and technical roadmap.

·       Evaluate and select suitable models, frameworks, vector databases and AI platforms.

·       Make practical build-versus-buy and open-source-versus-commercial decisions.

·       Ensure AI solutions are modular, reusable, scalable, secure and cost-effective.

2. Production AI Engineering

·       Lead AI initiatives from use-case assessment and proof of concept through production deployment.

·       Build reusable AI services, APIs, pipelines and platform components for web, mobile and internal applications.

·       Establish development, testing, deployment, monitoring and production-support practices.

·       Resolve complex issues involving model quality, retrieval, performance, reliability and cost.

3. LLM, RAG and Agentic AI

·       Design LLM applications, enterprise RAG solutions and controlled AI-agent workflows.

·       Build document processing, chunking, embedding, indexing, retrieval and response pipelines.

·       Implement source traceability, human approval and clear permissions for sensitive use cases.

·       Improve response quality through prompt design, retrieval optimisation and evaluation.

4. AI Quality, Security and Responsible AI

·       Define evaluation standards for accuracy, relevance, groundedness, safety, latency and cost.

·       Introduce benchmark datasets and regression testing for prompts, models and retrieval pipelines.

·       Implement safeguards for hallucinations, unsupported responses, data leakage and unsafe actions.

·       Ensure role-based access, encryption, audit logging, secure secrets handling and approved data usage.

5. AI Platform and Integration

·       Create common AI capabilities and integration standards that can be reused across products and teams.

·       Integrate AI services with applications, databases and approved enterprise data sources.

·       Implement observability for model usage, quality, errors, response time and cost.

·       Reduce dependency on a single model provider, individual engineer or external vendor.

6. Technical Leadership and Team Development

·       Define AI engineering standards for architecture, coding, review, testing and documentation.

·       Remain hands-on through design reviews, critical code reviews, debugging and focused prototypes.

·       Mentor AI/ML engineers, data engineers and application teams; support hiring and capability building.

·       Work with Product and business teams to convert high-value problems into measurable AI solutions.

Required Skills and Experience

·       10+ years of software engineering experience, including 5+ years in practical AI/ML engineering.

·       Proven experience moving AI solutions from prototype to reliable production use.

·       Strong Python and production API development experience, preferably FastAPI.

·       Strong knowledge of LLMs, RAG, prompt engineering, embeddings and agentic AI.

·       Experience with vector databases such as pgvector, Qdrant, Milvus or Weaviate.

·       Experience with LangChain, LlamaIndex, Haystack or similar orchestration frameworks.

·       Knowledge of cloud architecture (preferably AWS), microservices, databases, queues and distributed systems.

·       Experience with Docker, Kubernetes, CI/CD, observability and production support.

·       Strong understanding of application security, data privacy and responsible-AI engineering.

·       Demonstrated technical leadership, mentoring and stakeholder-management capability.

Good-to-Have Experience

·       Healthcare, medical education, EdTech or digital-learning platforms.

·       Medical-content intelligence, knowledge graphs, ontologies, taxonomy or semantic search.

·       Clinical Case Library, personalised learning or recommendation systems.

·       Multi-modal AI involving text, audio, images or video.

·       Healthcare-data privacy, de-identification or standards such as SNOMED CT, ICD, HL7 or FHIR.

How Success Will Be Measured

·       Production adoption and measurable business or learner impact of AI capabilities.

·       Accuracy, groundedness, reliability, security and cost efficiency of AI solutions.

·       Reusable AI services delivered across products with reduced technical and vendor dependency.

·       Improved capability, engineering quality and ownership within the AI team.

We are looking for a hands-on AI engineering leader who can distinguish a valuable production capability from an attractive demo and build AI that is secure, scalable, measurable and maintainable.

About the Company

As the world moves toward AI-native applications, one element matters more than anything: data.

We are building a platform that helps organizations unlock the value hidden within customer feedback. By centralizing data from multiple sources—including CRM platforms, support systems, collaboration tools, surveys, community forums, and customer conversations—into a unified intelligence layer, we enable teams to understand customer needs and make better product decisions.

Built on top of this foundation is an AI-powered platform that allows teams to query, monitor, and act on customer intelligence directly within their workflows. Our customers include some of the fastest-growing product-led technology companies as well as large global enterprises.

Backed by leading global venture capital firms, our engineering culture is AI-native from the ground up. Agents, LLMs, and applied machine learning are not side projects—they are central to how we build products. We operate at the cutting edge of agentic systems and continuously push the boundaries of what modern AI applications can achieve.

What You Will Do

• Convert product direction into phased technical plans, including estimates, milestones, dependencies, and risk assessments.
• Make thoughtful trade-offs across correctness, latency, user experience, AI/model behavior, reliability, cost, and delivery speed.
• Define clear ownership boundaries across frontend applications, BFF layers, backend services, agent workflows, ML platforms, and data contracts.
• Review ERDs, PRDs, design documents, and implementation plans to identify gaps before they become execution challenges.
• Drive planning and execution for complex initiatives while empowering area leads and senior engineers to maintain ownership.
• Partner closely with Product, Design, Engineering, and GTM teams to turn ambiguous problem statements into executable solutions.
• Influence technical direction through strong engineering judgment, architectural leadership, and hands-on involvement.
• Mentor senior engineers and contribute to engineering excellence through reviews, incident analysis, and proactive risk management.

What It Takes

• 9+ years of experience as a Senior Engineer, Technical Lead, or equivalent role.
• Demonstrated experience owning architecture and execution for large-scale product or platform initiatives spanning multiple teams.
• Strong foundation in distributed systems and modern software architecture.
• Exposure to agentic AI systems, machine learning engineering, MLOps platforms, and frontend/BFF architectures.
• Ability to own end-to-end product and technical problems rather than specializing exclusively in a single layer of the stack.
• Proven track record of converting ambiguity into actionable engineering plans.
• Excellent written communication skills, including design documentation, architectural reviews, trade-off analyses, and RCA documentation.
• Ability to challenge requirements constructively and collaborate effectively across Product, Design, Engineering, and GTM functions.
• High ownership mindset, attention to detail, and a low-ego, collaborative approach.

Strong AI Forward Deployed Lead/Architect Profile owning the full journey from client discovery to production, hands-on and client-facing

2

Mandatory (Experience 1): Must have 7+ years of experience, with hands-on production AI/ML work (LLMs, NLP, computer vision or advanced analytics)

3

Mandatory (Experience 2): Must be deeply hands-on, able to build SLMs, agentic structures, RAG pipelines, LLM orchestration and multi-modal systems, and get into cost/credit optimisation detail, not just high-level oversight

4

Mandatory (Experience 3): Must have 4+ years client-facing as a trusted AI advisor to CXOs/VPs of Operations/IT leaders, with a proven presales/solution-consulting track record (technical discovery, use-case scoping, solution design, ROI framing, PoC demos, blueprints, RFP/RFI responses)

5

Mandatory (Experience 4): Must have run workshops, AI readiness assessments and value-stream-mapping sessions, navigated ambiguous problem spaces, and built long-term client relationships that lead to referrals and expanded mandates

6

Mandatory (Experience 5): Must have designed and deployed end-to-end AI systems (ingestion, pipelines, deployment, monitoring, iteration), owning production constraints, integration, change management, go-live, client acceptance and post-deployment success, owning outcomes and fixing what breaks

7

Mandatory (Experience 6): Must have worked directly with enterprise-level clients in a client-facing capacity

8

Mandatory (Experience 7): Must have built and led their own team, creating a pod structure per project, and mentored junior AI engineers and consultants

9

Mandatory (Tech skill 1): Must be hands-on with Python, LLM frameworks (LangChain/LlamaIndex/HuggingFace) and cloud AI (AWS/Azure/GCP)

10

Mandatory (Skill 1): Must have strong storytelling, making complex AI accessible to business audiences without dumbing it down, and define AI architecture patterns and bring emerging frameworks/research back to the team

11

Mandatory (Skill 2): Must take strong ownership, be genuinely enthusiastic and client-facing (not a pure-research or low-energy profile), thrive in ambiguity and 0 to 1 environments, and want to join long-term as part of the company (ESOP pool)

12

Mandatory (Company): Must have experience across both services and product (this is a services-plus-product hybrid; candidates keen on pure product only are not a fit)

13

Mandatory (Background): Must come from an AI/ML, data analytics or data-platform company with enterprise client delivery, ideally with startup or growing-company experience having seen AI build cycles in 0 to 1 / ambiguous environments; Supply Chain AI exposure, or manufacturing/logistics, B2B SaaS AI or MLOps background, a plus

14

Mandatory (Pedigree): Must have strong pedigree, ideally IIT, then BITS/NITs/IIITs, with a CSE/IT background; strong equivalent capability also considered

AI Solution Architect

Experience: 12+ Years

Location: Coimbatore / Hybrid

Joining: Immediate / Within 30 Days

Primary Skills: Enterprise Architecture, Python, REST APIs, Microservices, Distributed Systems, Cloud (AWS/Azure/GCP), CI/CD, Docker, Kubernetes, Enterprise Data Architecture, Integration Patterns.

Preferred Skills: LLM Integrations, RAG Pipelines, AI Agents, Agentic Workflows, MCP (Model Context Protocol), Vector Databases, Prompt Engineering, LangChain/LlamaIndex, LLMOps, Multi-Agent Orchestration.

About the Role

At Techjays, we're helping companies reimagine how they build, operate, and scale with AI at the core. We focus on building secure, scalable, production-grade AI systems that solve real business problems and deliver measurable outcomes.

We're looking for an AI Solution Architect who can own enterprise-level solution architecture end-to-end. The ideal candidate combines deep technical expertise with the ability to understand complex business challenges, engage with prospective clients, and design compelling, scalable, AI-native solutions.

This is a senior technical role at the intersection of enterprise architecture, pre-sales, solutioning, and delivery governance. You'll collaborate with leadership, engineering teams, and clients to architect solutions spanning AI integrations, RAG pipelines, agentic workflows, cloud infrastructure, and enterprise platforms.

This role requires a strong technical background and current hands-on expertise. Candidates with purely pre-sales experience or non-technical solutioning backgrounds will not be a fit.

Key Responsibilities

• Own end-to-end enterprise architecture for AI-native platforms, defining high-level and low-level designs across backend systems, data platforms, and AI components.
• Lead solutioning for complex enterprise engagements, translating client requirements into technically sound, secure, scalable, and commercially viable architectures.
• Define reference architectures and design standards for LLM integrations, RAG pipelines, agentic workflows, MCP-based integrations, and enterprise data platforms.
• Partner with pre-sales and business development teams to prepare RFP responses, technical proposals, solution presentations, and architecture documents.
• Lead client discovery sessions and technical presentations, communicating architectural decisions and trade-offs to technical and executive stakeholders.
• Conduct architecture reviews and technical assessments to ensure scalability

About the Role

Antier Solutions is looking for a technically strong AI Solution Architect to drive the architecture, design, and technical execution of AI-powered solutions.

The role requires a combination of deep technical expertise, architectural ownership, and the ability to guide engineering teams in building scalable, production-ready AI systems. The individual will evaluate business problems, define technical approaches, make architecture decisions, and work closely with developers and stakeholders to translate ideas into working solutions.

This is a hands-on architecture and technical leadership role, not one limited to documentation, project coordination, or pre-sales presentations.

Key Responsibilities

1. AI Architecture & Solution Design

• Design end-to-end architectures for AI, Generative AI, LLM-based applications, and agentic AI systems.
• Evaluate and select appropriate models, frameworks, orchestration approaches, and technology stacks.
• Design solutions involving RAG, AI agents, workflow automation, APIs, databases, and enterprise integrations.
• Define architecture standards covering scalability, security, performance, maintainability, and deployment.
• Evaluate technical feasibility, identify risks, and recommend practical implementation approaches.

2. Technical Leadership & Implementation

• Provide architectural direction and technical guidance to AI engineers and developers.
• Review proposed designs and implementation approaches, identifying technical gaps and potential bottlenecks.
• Establish reusable architecture patterns, development standards, and engineering best practices.
• Troubleshoot complex technical challenges and contribute hands-on where necessary.
• Guide teams in adopting AI-assisted development and agentic workflows where they improve engineering productivity.

3. Production Readiness & Quality

• Design solutions with appropriate authentication, access controls, logging, monitoring, testing, and evaluation mechanisms.
• Address AI-specific challenges such as hallucinations, retrieval quality, model selection, latency, reliability, and inference costs.
• Define appropriate human oversight and validation mechanisms for sensitive or consequential workflows.
• Guide deployment decisions across suitable cloud, on-premise, and hybrid environments.

4. Business Alignment & Delivery Ownership

• Understand business requirements and translate them into implementable technical solutions.
• Work with stakeholders to establish scope, dependencies, estimates, resource requirements, and delivery milestones.
• Collaborate with engineering and delivery teams to move solutions from proof of concept to production.
• Balance rapid execution with architectural quality, maintainability, and commercial feasibility.
• Communicate technical decisions, risks, and trade-offs clearly to business and technical stakeholders.

Required Skills & Experience

• B.Tech, BE, MCA, MSc, or equivalent technical qualification in Computer Science, IT, Data Science, or a related discipline.
• 8+ years of overall experience in software engineering, AI engineering, solution architecture, or related technical roles.
• Strong hands-on experience building AI/ML or Generative AI applications, with demonstrated responsibility for architecture and implementation decisions.
• Strong understanding of LLMs, RAG pipelines, agentic AI systems, tool calling, orchestration, and AI-driven automation.
• Experience with Python and relevant AI frameworks, libraries, and integration technologies.
• Strong understanding of APIs, databases, data pipelines, distributed systems, and application architecture.
• Experience evaluating AI solutions for accuracy, reliability, performance, security, and cost.
• Ability to guide developers, review technical approaches, resolve complex problems, and establish reusable engineering practices.
• Experience deploying or supporting AI applications in production environments.
• Strong analytical, communication, and technical decision-making skills.

Preferred Qualifications

• Experience architecting multi-agent systems or complex AI automation workflows.
• Exposure to cloud platforms, containerization, CI/CD, observability, and scalable AI infrastructure.
• Experience mentoring engineering teams and introducing new AI development practices.
• Experience with enterprise AI deployments, international clients, or technically complex business environments.

Ideal Candidate Profile

• We are looking for an individual who can independently own technical architecture, guide engineering teams, and make sound implementation decisions. The candidate should combine hands-on AI expertise with the ability to turn business requirements into scalable, production-ready solutions.
• Candidates whose experience is limited to AI research, business analysis, project coordination, or pre-sales documentation without meaningful technical implementation and architecture ownership may not be suitable.