AI Success & Solutions (Lead / Manager / Director)

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

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 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.

Director of Engineering (AI Solutions & Delivery)

About the Role

We are looking for a Director of Engineering to lead engineering execution and delivery across our enterprise AI solutions. This role combines strong engineering leadership with operational discipline, ensuring AI products move from technical design to production with predictable timelines, high quality, and reliable performance.

You will lead Engineering Managers and Tech Leads, oversee multiple engineering teams, and drive delivery excellence across AI-powered applications and platforms. The focus is on execution, technical quality, and organizational accountability, ensuring teams deliver scalable, production-ready AI systems without compromising reliability or engineering standards.

Key Responsibilities

1. Engineering Delivery & Execution

• Own engineering delivery schedules, translating product roadmaps into achievable sprint plans and release milestones.
• Establish measurable delivery targets, aiming for at least 85% on-time delivery across engineering teams.
• Identify and resolve cross-team dependencies, technical bottlenecks, and resource constraints before they affect delivery.
• Drive engineering process improvements across Agile development, CI/CD, automated testing, and release management.
• Monitor delivery metrics, sprint commitments, and release readiness to maintain predictable execution.

2. AI Quality, Evaluation & Governance

• Establish automated AI evaluation pipelines to measure hallucinations, relevance, groundedness, and contextual accuracy before production releases.
• Define quality benchmarks and evaluation criteria for LLM-powered applications and AI-driven workflows.
• Implement safety mechanisms, validation filters, circuit breakers, and human-in-the-loop fallbacks to manage unpredictable AI outputs.
• Oversee RAG pipelines, vector databases, data ingestion, and retrieval mechanisms to maintain accurate, reliable, and compliant context.
• Reduce production AI quality regressions through continuous evaluation, monitoring, and feedback loops.

3. Architecture, Scalability & Reliability

• Oversee the architecture of distributed systems and cloud-native applications to support scalability, availability, and performance.
• Drive reliability targets, including 99.9% uptime and low time-to-first-token (TTFT) latency for applicable AI applications.
• Partner with Staff and Principal Engineers to design resilient systems, including fallback mechanisms for model API failures, timeouts, and rate limits.
• Establish engineering standards for code reviews, observability, automated integration testing, and security compliance.
• Ensure production systems meet defined service-level objectives (SLOs) and performance benchmarks.

4. Engineering Leadership & Team Development

• Lead and develop Engineering Managers and Tech Leads, establishing clear ownership, accountability, and delivery expectations.
• Build a high-performance engineering culture focused on technical excellence, collaboration, and continuous improvement.
• Partner with Talent Acquisition to attract, assess, and hire strong software engineering and AI talent.
• Align engineering resources with business priorities, delivery commitments, and technical requirements.
• Address performance and execution challenges while maintaining team engagement and engineering quality.

Success Metrics: First 90 Days

First 30 Days

• Assess existing engineering workflows, delivery pipelines, and release processes.
• Establish baseline AI evaluation criteria and identify gaps in production quality.
• Take ownership of engineering delivery across teams and align expectations with Engineering Managers and Tech Leads.

First 60 Days

• Implement automated AI evaluation frameworks and establish measurable quality benchmarks.
• Improve cross-team coordination and resolve key delivery dependencies.
• Establish consistent sprint planning, release cadences, and delivery reporting.

First 90 Days

• Achieve at least 85% sprint commitment completion.
• Demonstrate measurable reductions in production AI quality regressions.
• Improve system reliability and stabilize latency against defined performance targets.
• Establish clear accountability and predictable delivery practices across engineering teams.

Qualifications & Requirements

• Experience: 14+ years in software engineering, including at least 5 years managing Engineering Managers or Tech Leads in enterprise or high-growth environments.
• AI/ML Expertise: Hands-on experience delivering AI-powered systems involving LLMs, RAG architectures, vector databases, prompt engineering, or model fine-tuning workflows.
• Engineering Delivery: Proven ability to lead multiple engineering teams and deliver complex technical initiatives from design through production launch.
• System Architecture: Experience with distributed systems, cloud-native backend infrastructure, and scalable APIs across AWS, GCP, or Azure.
• AI Quality & Reliability: Understanding of AI evaluation frameworks, production monitoring, hallucination mitigation, guardrails, and system reliability.
• Delivery Management: Strong experience with Agile methodologies, CI/CD, release planning, and engineering performance metrics.
• Leadership: Ability to establish accountability, manage competing priorities, and deliver against ambitious deadlines without compromising quality or team morale.

What Success Looks Like

The successful candidate will establish predictable engineering delivery, improve the quality and reliability of production AI systems, and build a culture of ownership across engineering teams. They will combine technical judgment with execution-focused leadership to ensure AI solutions are delivered on schedule, operate reliably, and meet enterprise expectations.

If you are passionate about building high-performing engineering teams and delivering enterprise-grade AI solutions at scale, we would like to hear from you.

AI Project Manager

About Techjays

At Techjays, we are driving the future of artificial intelligence with a bold mission to empower businesses worldwide by helping them build AI solutions that transform industries. As an established leader in the AI space, we combine deep expertise with a collaborative, agile approach to deliver impactful technology that drives meaningful change. Our global team consists of professionals from leading companies such as Google, Akamai, NetApp, ADP, and Cognizant.

Role Overview

We are seeking a seasoned Project Manager to oversee the successful delivery of complex AI-driven projects. You will be responsible for bridging organizational goals with cross-functional execution, ensuring that our technical teams deliver high-quality solutions on schedule and within budget.

Key Recruitment Details

• Experience Required: 8 + Years (Minimum 6+ years in end-to-end project management)
• Work Location: Coimbatore / Hybrid
• Target Joining Date: (ASAP / Within 30 days)

Must-Have Qualifications

• Proven Execution: 6+ years of experience managing the complete lifecycle of enterprise-grade software projects.
• Agile Expertise: Advanced proficiency in Agile/Scrum methodologies, with a track record of facilitating sprint planning, stand-ups, and retrospectives.
• Technical Proficiency: Mastery of project management tools including Jira, MS Project, or Trello.
• Strategic Planning: Demonstrated experience in resource allocation, risk mitigation, and contingency planning.
• AI Enthusiasm: Experience leveraging AI tools in project management or a demonstrated passion for adopting AI technologies to optimize workflows.
• Stakeholder Management: Exceptional communication skills for managing both internal cross-functional teams and external client relationships.

Roles & Responsibilities

• Project Governance: Plan, execute, and monitor projects to ensure alignment with client expectations and organizational benchmarks.
• Team Leadership: Facilitate high-performing Agile environments, fostering a culture of collaboration, innovation, and technical ownership.
• Risk & Budget Management: Identify potential bottlenecks early, implement mitigation strategies, and manage project budgets to ensure optimal resource utilization.
• Reporting & Transparency: Track project progress using modern metrics, preparing detailed reports and presentations for senior management.
• Continuous Improvement: Analyze project outcomes to implement best practices and drive operational discipline across the engineering organization.
• Mentorship: Elevate team members through active support and guidance, ensuring professional growth within the team.

Success Milestones

Timeline

Expectations

First 30 Days

Audit active project pipelines, establish stakeholder relationships, and assume management of ongoing sprint cycles.

First 60 Days

Optimize resource allocation workflows, streamline cross-team communication, and implement improved risk-tracking mechanisms.

First 90 Days

Achieve >85% sprint commitment completion rate and stabilize project delivery predictability across assigned teams.

What Techjays Offers

• Competitive Compensation: Best-in-class packages structured to reflect experience and impact.
• Health & Wellness: Medical insurance covering self and family up to ₹4 lakhs per person.
• Flexibility: A trust-based time-off policy and a flexible hybrid work model.
• Environment: A casual work culture with a diverse, multicultural team that respects ideas regardless of designation.
• Growth: Meaningful professional development in an innovation-driven culture.

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.

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, 

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.

Bachelor’s degree in Engineering, Computer Science, or a related discipline

• 8+ years of experience in project or programme management

• 3+ years of experience delivering AI, Machine Learning, Data, or Analytics programmes

• PMP, Agile, Scrum, or equivalent certification preferred