Forward Deployment Engineer

Forward-deployed engineers (FDEs) are Mactores' services layer. You embed with the customer's team, own outcomes from discovery through the production cutover, and personally carry the delivery commitment.

The agent platform we deploy absorbs 60–70% of engagement work, discovery, assessment, design, and testing. You absorb the judgment: target architecture, refactoring trade-offs, model selection, cutover strategy, and the decisions an agent platform cannot make. The agent absorbs scale. You absorb judgment. 

This is not a staff-augmentation seat and not an advisory role. You ship.

What you will do?

• Deliver production agentic AI systems and AWS modernization engagements on committed dates across three pillars: Data Platform Modernization, Application & Database Modernization, and AI Agents for Apps.
• Build and productionize AI agents, orchestration, retrieval pipelines, evaluation harnesses, observability running against real customer data, not demo data.
• Convert existing products into agents: expose product functionality as callable tools for agent-to-agent composition, or replace form-and-click UX with agent-native, intent-driven interfaces.
• Convert existing Business processes into agents: expose process functionality as callable tools for agent-to-agent composition, or replace form-and-click UX with agent-native, intent-driven interfaces.
• Embed directly with customer engineering teams. Run architecture sessions, defend design decisions, and align stakeholders from VP Engineering to CTO.
• Make agent decisions traceable and defensible, validation runs in parallel with live workloads, and outputs hold up to internal audit and regulators (HIPAA, PCI-DSS, FSI-grade governance where the vertical demands it).
• Feed field experience back into the platform and practice: your deployment patterns, integration playbooks, and edge cases shape how we deliver.

What are we looking for?

• Excellent communication skills (English) — verbal and written. Non-negotiable. You will present architecture to customer CTOs, write documents that hold up in audit, and defend judgment calls in the room. If you can build but not explain, this role is not a fit.
• You have shipped production agentic AI systems on AWS. Not POCs, not notebooks — systems running in production for real users. This is the primary qualification. Be prepared to walk through what you shipped, the decisions you made, and what broke.
• Deep understanding of agentic architecture — you can design an agent system from first principles and explain why each component exists:
• Agent design patterns: single-agent vs. multi-agent systems, supervisor/orchestrator patterns, hierarchical agent topologies, planner–executor separation, and when each applies.
• Orchestration: building and operating orchestrator agents that decompose tasks, route work to specialist agents or tools, and manage state across multi-step workflows (LangGraph, Strands Agents, CrewAI, or equivalent).
• Memory: short-term/working memory (context management, conversation state) and long-term memory (episodic and semantic stores, vector- and graph-backed retrieval), and the production trade-offs of each.
• Reflection and self-correction: critique loops, self-evaluation, retry-with-feedback patterns, and evaluation harnesses that catch agent failures before customers do.
• Tool use and function calling: schema design, tool-selection reliability, error handling, and agent-to-agent composition.
• RAG and retrieval pipelines: chunking, embedding, hybrid retrieval, reranking, and grounding agent decisions in customer data.
• Strong AWS production experience: Amazon Bedrock and AWS AI services, plus core platform services (Lambda, API Gateway, DynamoDB, RDS/Aurora, Glue, EMR, Redshift, Kinesis, or similar depending on specialization).
• Solid software engineering fundamentals Python, TypeScript, CI/CD, infrastructure-as-code, testing-driven development discipline.
• Experience with data or application modernization (database migration, legacy refactoring, data platform builds) is a strong plus, since agents run against these workloads.
• Indicative experience: roughly 3–10 years in engineering roles, with agentic AI / GenAI as your current day job. We have demonstrated agent-native expertise over tenure — an engineer with 3–4 years of hands-on agentic AI work typically outperforms a 12-year generalist on this work.

You'll be preferred if you've:

• US English verbal and written fluency 
• Delivery experience in one or more of our verticals: Financial Services, Healthcare & Life Sciences, Internet & Software, Manufacturing, or Telco/Media/Entertainment/Gaming/Sports.
• Model tuning and fine-tuning: systematic prompt engineering and optimization; parameter-efficient fine-tuning (LoRA/QLoRA or similar); instruction tuning; working knowledge of RLHF/DPO; sound judgment on when to fine-tune vs. prompt vs. RAG; and evaluation of tuned models against baselines. Fine-tuning experience on Amazon Bedrock or SageMaker is a plus.
• Experience with compliance-sensitive AI systems (HIPAA, PCI-DSS, SOC 2, data residency).
• Knowledge graph, code-analysis (AST), or CDC/streaming experience (Debezium, Kafka/MSK).
• Solid software engineering fundamentals — Java, C++, Go Lang, .Net, Rust
• Prior customer-facing consulting or forward-deployed experience.
• AWS certifications (Solutions Architect Professional, Machine Learning Specialty, or Data Analytics).

Why This Role?

• You own outcomes, not tickets. FDEs carry the delivery commitment personally — architecture, judgment, and cutover are yours.
• You work agent-native from day one. Our delivery model would not function without agents. You build with the platform, not around it.
• You ship. Engagements measured in weeks to production, legacy retired, outcomes named. No archived pilots.
• You compound. Field delivery informs the Aedeon platform roadmap; the platform's growth expands what you can deliver. Few engineering roles sit in that loop.

About the Role

We are looking for a dynamic and customer-focused Presales Engineer – Cloud & AI to support our Cloud Transformation, Modernization, Managed Services, and AI/GenAI practices.

This role is pivotal in shaping technical solutions for prospective customers, driving value-based conversations, and bridging the gap between customer challenges and Comprinno's cloud and AI capabilities.

As a Presales Engineer – Cloud & AI, you will work closely with Sales, Solution Architects, Data & AI teams, and Delivery teams to understand customer requirements, design high-level solutions, and articulate how Comprinno's offerings can accelerate digital transformation and business outcomes.

This is a high-impact role that combines cloud solutioning, technical consulting, AI awareness, customer engagement, and presales excellence.

Key Responsibilities

Customer Discovery & Solutioning

• Engage with prospective customers to understand business objectives, technical challenges, and transformation goals.
• Conduct discovery workshops, assessments, and technical discussions to identify cloud and AI opportunities.
• Translate business requirements into high-level cloud and AI solution architectures.
• Collaborate with Solution Architects and technical teams to develop scalable and cost-effective solutions.

Cloud Transformation Consulting

• Support customer initiatives across:
• Cloud Migration
• Application Modernization
• DevOps Transformation
• Managed Services
• Security & Compliance
• Resilience & Disaster Recovery
• Conduct cloud assessments and migration readiness evaluations.
• Prepare cloud cost estimates, modernization roadmaps, and optimization recommendations.
• Leverage AWS programs and frameworks such as:
• Migration Acceleration Program (MAP)
• AWS Well-Architected Framework (WAFR)
• Foundational Technical Review (FTR)

AI & GenAI Solution Consulting

• Identify opportunities for AI, GenAI, and Agentic AI adoption within customer environments.
• Participate in AI discovery workshops and business value discussions.
• Support solution design for AI-powered applications leveraging AWS AI services.
• Collaborate with Data & AI teams to position analytics, machine learning, and Generative AI offerings.
• Assist customers in identifying suitable use cases for:
• Generative AI
• AI Assistants
• Knowledge Management Solutions
• Intelligent Automation
• Conversational AI

Proposal Development & Technical Presales

• Collaborate with Solution Architects to create:
• Technical Proposals
• Architecture Diagrams
• Effort Estimates
• Statements of Work (SOWs)
• Technical Responses to RFPs/RFIs
• Present solutions, recommendations, and technical approaches to customer stakeholders.
• Support Proof of Concepts (PoCs), demonstrations, and technical validations.
• Participate in technical reviews and solution walkthroughs.

Customer Engagement & Thought Leadership

• Attend customer meetings jointly with Sales teams to provide technical expertise.
• Build credibility and trust with customer technical and business stakeholders.
• Contribute to webinars, workshops, technical presentations, and customer-facing events.
• Write blogs, case studies, solution briefs, and technical content that supports business growth.
• Maintain an active presence in cloud and AI communities.

Collaboration & Knowledge Management

• Collaborate with Delivery teams to ensure smooth handover and alignment between presales and execution.
• Maintain reusable presales assets, templates, assessment frameworks, and knowledge repositories.
• Continuously stay updated on AWS services, AI advancements, partner programs, and emerging technology trends.
• Contribute to internal capability building and knowledge-sharing initiatives.

Travel & Customer Interaction

• Travel as required for customer meetings, workshops, assessments, events, and strategic engagements.
• Represent Comprinno professionally during all customer-facing interactions.

Required Qualifications & Skills

• Bachelor's degree in Computer Science, Information Technology, Engineering, or a related field.
• 3–5 years of experience in Presales, Solution Consulting, Cloud Consulting, Technical Sales, or related customer-facing roles.
• Strong understanding of AWS cloud services across:
• Compute
• Networking
• Storage
• Security
• Databases
• Knowledge of cloud migration and modernization frameworks such as AWS MAP.
• Familiarity with AWS cloud assessment tools including Migration Evaluator and AWS Well-Architected Tool.
• Understanding of:
• Kubernetes
• Containerization
• Cloud-Native Architectures
• DevOps Practices
• Hands-on exposure to Infrastructure as Code using Terraform or CloudFormation.
• Ability to create:
• Solution Documents
• Architecture Diagrams
• Technical Proposals
• Effort Estimates
• Statements of Work
• Understanding of Generative AI concepts including:
• Large Language Models (LLMs)
• Prompt Engineering
• Retrieval-Augmented Generation (RAG)
• AI Assistants
• Agentic AI Concepts
• Familiarity with AWS AI services such as:
• Amazon Bedrock
• Amazon SageMaker
• Amazon Q
• AWS Solutions Architect – Professional Certification.
• Experience positioning AI, GenAI, Analytics, or Data Engineering solutions.
• Familiarity with AI frameworks such as:
• LangChain
• LangGraph
• CrewAI
• OpenAI APIs
• Anthropic APIs
• Strong communication, presentation, and stakeholder management skills.
• Experience conducting AI assessments, GenAI workshops, or AI proof-of-concepts.
• Experience contributing to webinars, blogs, case studies, or technical marketing content.
• AWS Solutions Architect – Associate Certification (Mandatory).
• Customer-centric mindset with strong problem-solving abilities.

Desired Qualifications & Skills

• AWS Specialty Certifications such as:
• Security Specialty
• Advanced Networking Specialty
• Machine Learning Specialty
• DevOps Engineer Professional
• Understanding of compliance frameworks such as:
• ISO 27001
• SOC 2
• HIPAA
• MBA or equivalent business qualification.
• Contributions to industry blogs, webinars, conferences, technical communities, or open-source initiatives.

What We're Looking For

• Strong consulting mindset with the ability to understand customer business challenges.
• Excellent communication and presentation skills.
• Ability to balance technical depth with business value articulation.
• Passion for cloud technologies, AI, and emerging innovation.
• Collaborative team player who can work across Sales, Architecture, Delivery, and Product teams.
• High ownership and accountability.
• Curiosity and continuous learning mindset.
• Willingness to travel and work from customer locations across India and internationally as required by project engagements.

What Success Looks Like

• Consistently contributes to qualified pipeline creation and deal progression.
• Produces high-quality proposals, architecture recommendations, assessments, and Statements of Work.
• Builds credibility with customers through strong technical consulting and solution design.
• Successfully supports closure of Migration, Modernization, Managed Services, Security, and AI opportunities.
• Develops reusable presales assets, assessment frameworks, and solution accelerators.
• Contributes to thought leadership through blogs, webinars, technical presentations, and customer-facing content.
• Continuously expands expertise in AWS, Cloud, AI, and emerging technologies.

Why Join Comprinno

• Work at the forefront of Cloud, AI, and AWS innovation with one of the leading AWS Partners in APJ.
• Be part of real transformation initiatives involving Migration, Modernization, DevOps, Security, Managed Services, and Generative AI.
• Collaborate directly with enterprise customers and influence strategic technology decisions.
• Accelerate your growth through AWS certifications, mentorship, and hands-on customer engagements.
• Contribute to innovative solutions including Tevico and future AI-powered offerings.
• Build thought leadership through webinars, blogs, events, and community participation.
• Join a collaborative, high-performance culture that values ownership, innovation, and continuous learning.

Roles & Responsibilities

What you'll do

• Embed with enterprise customers to understand their business processes, systems landscape, and data. Translate that into concrete use cases for AI agents, automations, and applications.
• Build working solutions on the UnifyApps platform yourself, using the no-code builders, integrations, and data layer. Ship, iterate, and get to production.
• Own the technical and functional design of deployments across systems like Salesforce, Workday, SAP, ServiceNow, core banking and telecom stacks.
• Work with customer stakeholders from process owners up to CXOs. Run discovery workshops, demos, solution reviews, and go-live.
• Define success metrics for each deployment and hold the deployment to them.
• Feed patterns and gaps back to the core product and engineering teams. Turn repeated custom work into platform capability.
• Support pre-sales and pilot conversations where deep product depth is needed.

Ideal Candidate

1Strong Forward Deployed Product Manager Profile with robust exposure to client facing delivery

2Mandatory (Experience 1) - Must have 4+ years of product management experience with a track record of shipping successful products with product companies

3Mandatory (Experience 2) Must have hands-on experience driving platform deployment and integrations for enterprise clients — acting as the technical/product owner to understand client requirements, design the solution, and connect the platform with enterprise systems, APIs, and data sources.

4Mandatory (Tech skill 1) - Must have strong technical aptitude with hands-on experience in GenAI and API integrations

5Mandatory ( Skill 1): Must have experience working closely with designers, engineers, testers and other stakeholders

6Mandatory (Skill 2): Must be very good at making impactful presentations, with strong communication and presentation skills

7Mandatory (Skill 3) - Must have a bias to build, with comfort in ambiguity and evolving scope

8Mandatory (Skill 3): Must be able to lead and mentor a team of Forward Deployed Engineers, helping them grow into future product leaders

9Mandatory (Company) - Tier1 B2C product companies or Series B+ funded B2B product companies

10Preferred (AI Depth) - Exposure to enterprise AI deployments in production, including evaluation, guardrails, and observability of agents.

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.

Immediate Joiner - 15 days Notice period

• Primary Skillsets: Python, Generative AI, Agentic AI, LLM, experience with designing and developing production grade AI solutions, AWS/Azure/GCP, LLMOps, Data Engineering (Databricks/Snowflake), Regulated GenAI, Data and Decisions, and Agentic Systems.

• Preferred: Pharma or life sciences experience, especially in regulated (GxP) environments, experience setting up a team or practice from scratch.

• Primary Skillsets: Python, Generative AI, Agentic AI, LLM, experience with designing and developing production grade AI solutions, AWS/Azure/GCP, LLMOps, Data Engineering (Databricks/Snowflake), Regulated GenAI, Data and Decisions, and Agentic Systems.
• Preferred: Pharma or life sciences experience, especially in regulated (GxP) environments, experience setting up a team or practice from scratch.
• Only immediate joiners are considered.

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, 

Job Title: Senior AI Engineer

Experience: 5–10 years in software engineering, with 3+ years in AI/ML engineering preferred

Employment Type: Full-time

Department: Artificial Intelligence / Engineering

Location: Bangalore

We are looking for a highly skilled Senior AI Engineer to design, develop, deploy, and scale intelligent AI-powered applications and solutions. The ideal candidate will have strong expertise in machine learning, Generative AI, Large Language Models (LLMs), Retrieval-Augmented Generation (RAG), AI agents, and production-grade software engineering.

The candidate will work closely with product managers, data scientists, software engineers, and business stakeholders to translate complex business requirements into scalable, reliable, secure, and cost-effective AI solutions.

Key Responsibilities

1. AI Solution Design and Development

• Design, develop, and deploy end-to-end AI and machine learning solutions.
• Build intelligent applications using LLMs, Generative AI, NLP, and deep learning techniques.
• Develop scalable AI architectures and reusable components for enterprise applications.
• Evaluate AI models and select appropriate approaches based on accuracy, latency, cost, and business requirements.

2. Generative AI and LLM Engineering

• Develop LLM-powered applications using models from providers such as OpenAI, Anthropic, Google, or open-source ecosystems.
• Design and implement RAG pipelines, document processing workflows, semantic search, and knowledge retrieval systems.
• Build AI agents and multi-step workflows using appropriate orchestration frameworks.
• Implement prompt engineering, structured outputs, tool calling, and context management.
• Optimize model performance through evaluation, fine-tuning where appropriate, and inference optimization.

3. Machine Learning and Data Engineering

• Develop and operationalize machine learning models for classification, prediction, recommendation, and other business use cases.
• Work with structured, unstructured, and multimodal data.
• Build data preprocessing, feature engineering, model training, and validation pipelines.
• Apply appropriate model evaluation techniques and statistical methods.

4. Production Deployment and MLOps / LLMOps

• Deploy AI solutions using cloud platforms such as AWS, Microsoft Azure, or Google Cloud.
• Build automated CI/CD pipelines for AI applications and model deployment.
• Implement model versioning, experiment tracking, monitoring, and performance evaluation.
• Optimize inference latency, throughput, infrastructure utilization, and operating costs.
• Establish observability for AI applications, including token usage, retrieval quality, latency, failures, and model behavior.

5. Software Engineering and System Architecture

• Develop production-quality applications and APIs using Python and modern backend frameworks.
• Design RESTful APIs, asynchronous processing systems, and microservices where appropriate.
• Integrate AI services with enterprise applications, databases, and third-party systems.
• Use relational databases, vector databases, caching, and distributed systems effectively.
• Apply software engineering best practices, including testing, code reviews, documentation, and design patterns.

6. Security, Quality, and Responsible AI

• Implement safeguards against prompt injection, data leakage, insecure tool execution, and unauthorized access.
• Apply appropriate privacy, security, and compliance controls to AI systems.
• Evaluate hallucinations, bias, reliability, and failure modes.
• Design human-in-the-loop workflows and fallback mechanisms for critical use cases.

7. Technical Leadership and Collaboration

• Lead technical design discussions and mentor junior AI engineers.
• Define engineering standards, coding practices, and AI development guidelines.
• Collaborate with cross-functional teams to deliver measurable business outcomes.
• Conduct code reviews, evaluate emerging technologies, and recommend practical innovations.
• Communicate technical trade-offs and solution architecture to both technical and non-technical stakeholders.

Required Technical Skills

Programming and Backend Development

• Strong proficiency in Python.
• Experience with FastAPI, Flask, or Django.
• Knowledge of REST APIs, asynchronous programming, data structures, algorithms, and software design principles.
• Experience with Git, automated testing, and CI/CD.

AI and Machine Learning

• Strong understanding of supervised and unsupervised learning, model evaluation, and statistical concepts.
• Hands-on experience with PyTorch, TensorFlow, or scikit-learn.
• Knowledge of NLP, embeddings, transformers, and deep learning fundamentals.

Generative AI and LLMs

• Hands-on experience building production applications using LLM APIs or open-source models.
• Strong understanding of RAG architecture, embeddings, chunking, reranking, and retrieval evaluation.
• Experience with vector databases such as Pinecone, Weaviate, Qdrant, Milvus, or pgvector.
• Familiarity with orchestration frameworks such as LangChain, LangChain, or comparable tools.
• Experience with tool calling, structured generation, agent workflows, and LLM evaluation.

Cloud and Infrastructure

• Experience with AWS, Azure, or Google Cloud.
• Working knowledge of Docker and Kubernetes.
• Familiarity with model serving, scalable APIs, monitoring, and production deployments.
• Experience with MLOps or LLMOps practices.

Databases and Data Processing

• Strong SQL skills and experience with relational databases.
• Familiarity with NoSQL databases, data pipelines, and distributed processing.
• Experience working with unstructured data, document ingestion, and retrieval systems.

Preferred Qualifications

• Experience with fine-tuning, quantization, or serving open-source LLMs.
• Experience with multimodal AI, including text, image, audio, or video.
• Knowledge of agent evaluation, workflow orchestration, and multi-agent systems.
• Experience with AI security, guardrails, governance, and responsible AI.
• Experience optimizing AI workloads for latency, scalability, and cost.
• Contributions to open-source AI projects, research, or technical publications.

Education

• Bachelor's or Master's degree in Computer Science, Artificial Intelligence, Machine Learning, Data Science, or a related field.
• Equivalent practical experience and demonstrated engineering expertise may be considered.

Key Success Metrics

• Quality, accuracy, and reliability of deployed AI solutions.
• Production adoption and measurable business impact.
• Reduction in inference costs and response latency.
• RAG retrieval quality and reduction in unsupported or incorrect responses.
• System availability, scalability, and operational stability.
• Delivery timelines, code quality, and technical mentoring effectiveness.

Job Title: Java AWS Kubernetes DevOps AI/ML

Experience: 8–10 Years

The candidate should have at least 1 year of experience in AI/ML and hands-on experience with the below technologies:

Java

AWS

Kubernetes

DevOps

AI/ML

MongoDB / PostgreSQL

Key-Value Caching

Vector Databases – ChromaDB / Pgvector