Lead AI Engineer

Senior AI Engineer

Company: Techjays

Experience: 4–8 Years

Location: Coimbatore, Hybrid

Joining: Immediate or within 30 days preferred

About the Role

We are looking for a Senior AI Engineer with strong Python development skills and hands-on experience building and deploying AI-powered applications. The ideal candidate should have practical knowledge of LLMs, Retrieval-Augmented Generation (RAG), vector databases, and scalable backend systems.

You will work closely with engineering and product teams to design, develop, and deploy reliable AI solutions for real-world business problems.

Key Responsibilities

• Design, develop, and maintain scalable backend applications using Python and frameworks such as Django, Flask, or FastAPI.
• Build and integrate AI-powered applications using LLMs, RAG pipelines, and agentic AI workflows.
• Develop document processing, embedding generation, semantic search, and retrieval pipelines.
• Integrate LLM APIs and optimize prompts, response quality, latency, and cost.
• Work with vector databases and relational databases to build efficient data retrieval systems.
• Design and implement REST APIs, asynchronous processing, caching, and queuing mechanisms.
• Apply software engineering best practices, including design patterns, clean code, testing, and version control.
• Deploy and maintain applications on AWS or GCP using Docker and CI/CD pipelines.
• Monitor production systems, troubleshoot issues, and improve application performance and reliability.
• Collaborate with cross-functional teams to translate business requirements into practical AI solutions.
• Contribute to code reviews, technical documentation, and knowledge sharing within the engineering team.

Required Skills

• 4–8 years of experience in software development, with strong hands-on Python expertise.
• Strong understanding of data structures, algorithms, object-oriented programming, and design patterns.
• Experience building backend applications using Django, Flask, or FastAPI.
• Hands-on experience with LLM-based applications and API integrations.
• Practical experience implementing RAG pipelines, embeddings, and semantic search.
• Experience with vector databases such as Pinecone, Qdrant, Weaviate, or Chroma.
• Strong knowledge of SQL and relational databases such as PostgreSQL or MySQL.
• Experience with caching, message queues, asynchronous processing, and WebSockets.
• Familiarity with AWS or GCP, Docker, Git, and CI/CD pipelines.
• Understanding of API design, system architecture, application security, and performance optimization.
• Experience testing, debugging, and maintaining production-grade applications.

Preferred Skills

• Experience building agentic AI applications and multi-agent workflows.
• Familiarity with LangChain, LangGraph, LlamaIndex, or similar frameworks.
• Knowledge of Model Context Protocol (MCP) and tool-calling mechanisms.
• Experience with LLM evaluation, hallucination reduction, and output quality monitoring.
• Familiarity with fine-tuning, model selection, and inference optimization.
• Experience with LLMOps, AI observability, and production monitoring.
• Knowledge of Kubernetes and microservices architecture.

What We’re Looking For

• Strong problem-solving and analytical skills.
• Ability to take ownership of features from development through deployment.
• Practical understanding of how to build reliable AI applications beyond prototypes.
• Ability to make sound technical decisions and explain architectural trade-offs.
• Good communication and collaboration skills.
• Willingness to learn, experiment, and work on challenging AI engineering problems.

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.

Role Overview 

We are looking for an AI Engineer to design, build, and ship production AI systems, including agentic AI applications, for enterprise clients. This is a hands-on engineering role: you will write production code, build and evaluate models and agents, and work closely with architects and product teams to take solutions from prototype to scale. 

Key Responsibilities 

Design and build agentic AI systems: agent workflows, tool/function-calling, memory, and human-in-the-loop patterns. Build and productionise RAG pipelines, prompt-based applications, and LLM integrations across providers. Develop and maintain data and ML pipelines: feature engineering, model training, evaluation, and monitoring. Integrate AI systems with enterprise applications (CRMs, ERPs, ITSM tools) via APIs, events, and MCP-based tool servers. Implement guardrails, prompt-injection defences, and evaluation frameworks to keep AI systems safe and reliable in production. 

Write clean, tested, production-grade code and participate actively in code and design reviews. 

Collaborate with architects, product managers, and delivery teams to translate requirements into working AI solutions. Troubleshoot and optimise AI systems for accuracy, latency, and cost in production. 

Required Qualifications 

8–12 years of hands-on software engineering experience, with a strong, unbroken technical track record. Hands-on experience building and shipping AI/ML systems in production, not just POCs. 

Practical experience with agentic AI systems and at least one major agent framework (LangGraph, CrewAI, AutoGen, OpenAI Agents SDK, Bedrock Agents/Strands, or Semantic Kernel). 

Experience with LLM/GenAI systems: RAG pipelines, prompt engineering, structured outputs, and tool calling across providers. 

Strong Python skills (TypeScript/Node.js a plus), with production-grade testing, CI/CD, and API design practices. Working knowledge of ML fundamentals: model evaluation, feature engineering, and experimentation. Cloud-native experience on AWS and/or Azure: containers, serverless, event backbones, and vector databases. Understanding of LLM safety and reliability practices: guardrails, prompt-injection defences, and observability. 

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.

We're hiring Drumbeat's first Senior Product Engineer to help us go to the next level.

We're not first-timers. We're the team that founded Phrasee in 2015 and grew it to more than 100 people, customers around the world, and a >$100m exit. Lots of people made lots of money, including the founding employees.

With Drumbeat, our plan is to do it all again… except this time, bigger and faster.

What is Drumbeat?

Drumbeat (www.drumbeat.com) predicts your next best action on LinkedIn, then copilots your team to turn invites, DMs and posts into pipeline.

We launched in September 2025 and have raised a £2m seed round led by a well-known VC. We're building the core team around our cofounders (Parry, Jonny and Dan) and Mohit, our Engineering Manager and Phrasee's first engineer. You'll report to Mohit.

What you'll be building on

• Setlist: a short, ordered list of the right things for each person to do today.
• Radar: predicts who each rep should connect with now, not everybody who matches a job-title search.
• Riff and AuthorDNA: help people find something worth saying, and write it in their own voice.
• Campaigns, Conversations, Backstage and Analytics: team-wide activity, replies, company knowledge and reporting.

Under the hood: Django and PostgreSQL, background jobs on Redis, LLM workflows, statistical models that learn what works, and integrations with LinkedIn and CRMs. A lot of our code is written with AI coding agents, inside a strict engineering process.

How we think about engineering

1. "Human-first AI, not AI-first humans." AI agents write a lot of our code. They don't make our decisions. We want someone super smart without AI, and even smarter with it, who knows when the machine is blowing smoke.
2. "Spend 55 minutes on the problem." Understand what's actually wrong before you fix it.
3. "Fix the class, not the instance." Every bug is an example of something bigger. Find the pattern, and fix it everywhere it lives.
4. "A check must be able to fail." A test you've never seen go red proves nothing.
5. "You don't need a Ferrari to drive to the supermarket." Simplicity over cleverness, every time.

What you'll own

1. Technical architecture. How Drumbeat is built, so it holds up as we add customers, features and integrations.
2. Shipping features end to end. From Parry and Mohit's briefs, and turning Parry's MVPs into production features.
3. Foundations. Scale, resilience (safe retries, timeouts and fallbacks when outside services wobble), cost and refactoring.
4. What you build. Once it's live, it's yours: you maintain it and fix it.
5. The hardest bugs. The ones nobody else can crack.

Your experience

• 8+ years as an engineer, at a product company, preferably B2B SaaS.
• Time at a smaller company, so you know how to get stuck in.
• You've owned a system in production, not just added features to one.
• Senior Python and Django: Postgres, background jobs, correctness in production.
• System architecture and refactoring.
• You direct AI coding agents well, and explain trade-offs in plain English to people who aren't engineers.

Where you'll work

Delhi / NCR. You'll be in our Delhi office some days each week, alongside Mohit.

Pay

₹40–65 lakh CTC, plus equity that vests as the company hits revenue milestones.

This is probably not a job for most people. Don't apply if you want an easy job. Apply if you want a hard one: lots of responsibility, very little bureaucracy, plenty to figure out, and a genuine chance to shape what this company becomes… and make a bunch of money as a result.

If that sounds exciting, LET'S PARTY.

• We are seeking a high-velocity Senior Technical Lead with an overall experience
• of 12+ years in software engineering who thrives in an environment of autonomy
• and rapid iteration. At Nomiso, we don't just write code; we build resilient systems.
• This role is designed for a builder who embraces our core philosophy: Build, Ship,
• Measure, Improve. You will be responsible for taking features from a raw concept
• to a live environment, ensuring that every deployment delivers high technical
• excellence and system stability. You are not just an executor; you are a technical
• owner and system architect who understands that the "Ship" is only the midpoint of the system lifecycle.

What We’re Looking For (Who You Are)

● AI Systems Architect (5+ years in AI - LLM, RAG, Context Engineering, React, Prompt): You've shipped multiple production LLM systems at scale. Deep understanding of RAG, fine-tuning, prompt optimization, inference scaling, and cost management. You make pragmatic trade-offs between

accuracy, latency, and cost.You are accountable for the high quality solution and timely shipment of the feature as per committed timeline.

● Technical Lead Manager (TLM): In this role you will do 70% technical work and 30% people management & project management work. You've led engineering teams (4-8+ engineers). You grow engineers through mentoring, clear feedback, and ownership. You balance shipping speed with technical excellence and team development.

● The "Builder" Mentality: You have a bias toward action. You prefer a

working feature over a perfect specification and understand the value of

getting software into the hands of users early. You will not leave the tech

debt for a long time. Tech debt will be continuously addressed as product matures.

● Pragmatic Architect: You choose the right tool for the job, balancing

long-term scalability with the immediate need for speed and delivery.

● Cross-functional engagement: Partner with Product, QA, and Solution

Architects, Project manager to align execution with outcomes

● Continuous Learner: You view "Improvement" as a mandatory phase, not an optional one. You are eager to refactor and pivot based on objective evidence.

Requirement

AI Expertise

• LLM APIs (OpenAI, Anthropic, Hugging Face), model selection and trade-offs. RAG pipeline architecture, fine-tuning strategies, prompt engineering, Agentic patterns, multi-step reasoning, reliability under uncertainty

Fullstack depth

● Backend: Python (FastAPI,

Django) and Node.js (Express,

Nest.js)

● Frontend: React (TypeScript,

state management, performance

optimization)

● Databases: PostgreSQL (query

optimization, schema design,

scaling)

● Infrastructure: AWS (Lambda,

ECS, RDS, S3), Docker, CI/CD,

monitoring

Leadership

● Managed engineering teams

(4-8+ engineers)

Category Requirement

● Experience mentoring and

growing engineers

● Track record of building

high-performing teams

● Comfortable with ambiguity and

rapid decision-making

Experience 12+ years in a technical-focused

engineering environment.

Education Degree in CS or equivalent professional

experience.

You will operate within a cross-functional squad, collaborating daily with Solution

Architects and other engineering peers. You will ensure that features are released

safely and frequently, and own the success of the product, by analysing the

insights and user feedback. Our hierarchy is flat; you are expected to provide

technical leadership and mentorship to junior peers while having a direct line of

communication with leadership regarding technical strategy and technical debt.

About Orenda

Orenda is a fintech-enabled financial services company founded in 2017 and based in Ahmedabad, Gujarat. We work as a corporate DSA and channel partner for leading banks and NBFCs, helping individuals, professionals, and MSMEs access suitable financial solutions across business loans, personal loans, home loans, loan against property, and professional loans.

At Orenda, we combine financial expertise, technology, and streamlined processes to simplify the lending journey. We are building our own lending-tech platform to improve operational efficiency, strengthen lender and partner integrations, and develop technology-driven solutions that support our evolving business needs.

About the role

We're looking for a Lead Software Engineer who enjoys building as much as designing. You'll shape how our systems are architected, write a good share of the code yourself, and lead a team of 3 to 5 engineers, including AI/ML engineers.

This is a hands-on leadership role, not a pure management or architecture position. Your time will be roughly split like this:

Share of time

Leading the team ~30%

Solution design ~30%

Hands-on coding ~30%

Deployment and infrastructure~10%

What you'll work on

Solution design and architecture

• Turning business needs into technical designs that are practical to build and easy to maintain

• Making architecture decisions across services, integrations and data flows

• Estimating, scoping and planning new features with business and product stakeholders

• Shaping the solution design for AI/ML use cases alongside the AI/ML engineers

Engineering and delivery

• Writing production code in Python and Node.js

• Rolling out new features across our internal and partner-facing platforms, including our CRM and partner portal

• Continuously improving existing systems for performance, reliability and code quality

• Reviewing code and setting engineering standards for the team

Infrastructure and cloud

• Maintaining and upgrading our infrastructure, including a planned move to a major cloud platform

• Owning deployments and production stability together with the team

• Working with event-driven and messaging systems (NATS today, Kafka planned)

Team leadership

• Leading and mentoring a team of 3 to 5 engineers

• Planning and prioritising work, unblocking the team, and making sure things ship

• Building a culture of ownership, clear documentation and quality

What we're looking for

• 5 to 8 years of software engineering experience, with hands-on coding in your recent roles

• Strong command of Python and Node.js

• Proven experience designing and building backend systems and microservices that run in production

• Hands-on experience deploying and running workloads on at least one major cloud platform (AWS, Azure or GCP)

• Solid working knowledge of PostgreSQL

• Experience with messaging or event streaming systems such as NATS, Kafka or RabbitMQ

• Experience leading or mentoring a small engineering team

• Comfort estimating effort, breaking down features and planning delivery

• Enough understanding of AI/ML systems to guide their solution design and integration

• Clear communication with both technical and non-technical stakeholders

Good to have

• Experience working at a fintech, lending or financial services company

• Hands-on experience with Kafka

• Experience leading an infrastructure upgrade or cloud migration

• Experience building or scaling CRM or partner-facing portal products

Why Join Us?

• Build a growing lending-tech platform: Play a key role in shaping the technology behind a growing financial services business.
• Own technical decisions: Contribute directly to architecture, engineering standards, and technology direction.
• Stay hands-on: Combine solution design and technical leadership with meaningful coding responsibilities.
• Work with emerging technologies: Collaborate with AI/ML engineers and contribute to evolving platform capabilities.
• Lead and grow a team: Mentor engineers and help build a strong engineering culture as the technology function scales.
• Make a measurable impact: Build systems that improve operational efficiency and support business growth.

Role: Lead Engineer

Experience: 8+ Years (with strong AI/GenAI expertise)

About the Role

As a Senior / Lead AI Engineer, you will be responsible for architecting, developing, and deploying enterprise-scale AI solutions leveraging Large Language Models (LLMs), Retrieval Augmented Generation (RAG), and Agentic AI frameworks. You will work closely with cross-functional teams to deliver innovative AI products while driving best practices in AI engineering, governance, and operational excellence.

Key Responsibilities

• Design, build, and deploy enterprise-grade LLM, Generative AI, and Agentic AI solutions.
• Lead the architecture and implementation of multi-agent systems and complex AI workflows.
• Build and optimize RAG (Retrieval Augmented Generation) architectures for enterprise use cases.
• Fine-tune, optimize, evaluate, and improve AI/GenAI models for performance, accuracy, scalability, and cost efficiency.
• Integrate AI solutions with cloud platforms, APIs, enterprise applications, and backend systems.
• Establish and maintain AI engineering best practices including AgentOps, governance, observability, security, and responsible AI principles.
• Implement automated deployment strategies using modern CI/CD pipelines.
• Collaborate with business stakeholders, architects, product teams, and engineering teams to ensure successful project delivery.
• Mentor and guide engineering teams on AI solution architecture and implementation.

Required Experience

• 2+ years of hands-on experience building and deploying LLM, GenAI, or Agentic AI solutions in production environments.
• Strong experience designing and implementing multi-agent frameworks and orchestration systems.
• Experience with AI-assisted software development tools such as:
• GitHub Copilot
• Claude Code
• Codex
• Similar AI SDLC tools
• Strong expertise in RAG architecture design and implementation.
• Experience optimizing AI models, prompts, workflows, and inference pipelines.
• Hands-on experience integrating AI applications with enterprise ecosystems and cloud services.

Technical Skills

Programming & Frameworks

• Python
• LangChain
• LangGraph

Enterprise AI Platforms & Frameworks

• AgentBricks
• AgentCore (AWS)
• Microsoft Foundry
• Gemini Agent Studio
• Snowflake Cortex Agents

Cloud & AI Platforms

• AWS Bedrock
• Databricks Mosaic AI
• Microsoft Foundry
• Gemini Enterprise Agent Platform
• Snowflake Cortex

DevOps & Engineering

• Docker
• Kubernetes
• CI/CD Pipelines
• AI Governance
• MLOps / AgentOps best practices

Preferred Qualifications

• Experience in enterprise-scale AI transformation programs.
• Knowledge of Responsible AI, AI governance frameworks, and compliance standards.
• Experience leading technical teams and driving AI solution strategy.
• Exposure to observability, monitoring, evaluation frameworks, and AI platform engineering.

Soft Skills

• Excellent verbal and written communication skills.
• Strong stakeholder management and collaboration abilities.
• Ability to lead cross-functional teams and deliver business outcomes.
• Problem-solving mindset with a focus on innovation and continuous improvement.

Why EPAM?

• Work on cutting-edge AI and GenAI initiatives for global enterprises.
• Collaborate with highly skilled architects, engineers, and AI specialists.
• Exposure to large-scale digital transformation and innovation programs.
• Continuous learning opportunities through EPAM's world-class engineering culture.
• Opportunity to shape the future of enterprise AI solutions.

Most sales tools help you send emails. We’re building something different.

At Salesforge, we’re creating autonomous AI agents that can:

Find the right prospects

Generate highly personalized outreach

Run conversations

And book meetings

All without human involvement.

Why this is interesting

A lot of AI products stop at “generate text.” We’re focused on outcomes.

That means solving problems like:

How do you generate messages that actually get replies?

How do you evaluate and improve agent performance over time?

How do you orchestrate millions of AI-driven interactions reliably?

How do you combine structured data + LLMs in a way that scales?

If you enjoy working at the intersection of systems + AI + real-world feedback loops, this will feel like a playground.

What you’ll be working on

You won’t be maintaining legacy systems.

You’ll be:

Designing and building core backend systems that power our AI agents

Creating APIs and services that handle high-scale, real-time workflows

Working with queues (Kafka / SQS / RabbitMQ) to orchestrate async systems

Thinking deeply about performance, cost, and reliability in AI pipelines

Shipping features end-to-end with a small, senior team

The team

We’re a small group of experienced builders. We move quickly, care about quality, and avoid unnecessary process.

No layers of management.

No long planning cycles.

Lots of ownership and autonomy.

What we’re looking for

5+ years of backend engineering experience

Strong system design fundamentals

Experience with distributed systems and async processing

Familiarity with relational and/or document databases

Clear communicator, low ego, high ownership

Why join

You’ll work on a product where the output is measurable (meetings booked, revenue generated)

You’ll have real ownership from day one

You’ll be early in building a new category (AI sales agents)

You’ll grow as fast as we do

Strong Senior AI Engineer Profile with production-grade LLM and Python expertise

2

Mandatory (Experience 1): Must have at least 3+ years of professional AI/ML engineering experience with demonstrated track record of delivering production-grade AI systems in real-world environments

3

Mandatory (Tech skill 1): Must have strong programming skills in Python and SQL, with hands-on ML/data libraries (scikit-learn, pandas, numpy) and deep-learning frameworks (PyTorch or TensorFlow), plus REST API design

4

Mandatory (Experience 2): Must have hands-on experience building and deploying production-grade ML/LLMs including RAG pipelines, document parsing, information extraction, and text processing on large-scale unstructured data (preprocessing, chunking, embeddings, feature engineering)

5

Mandatory (Tech skill 2): Must have strong NLP / extraction-focused ML depth — transformers, embeddings, vector databases, RAG, LLM integrations, and agentic workflows.

6

Mandatory (Tech skill 3): Must have hands-on experience with AWS (SageMaker, Bedrock, EC2, Lambda) and Azure (AI Foundry, Azure OpenAI) for model training, fine-tuning, deployment, and inference at scale

7

Mandatory (Tech skill 4): Must have experience with multi-agentic frameworks / orchestration tools (Claude Code, LangGraph, LangChain, CrewAI)

8

Mandatory (Tech skill 5): Must have hands-on experience with MLOps ecosystem including experiment tracking (MLflow, Weights & Biases), model versioning/registry, automated retraining, and CI/CD (GitHub Actions, Azure DevOps, Docker, Kubernetes)

9

Mandatory (Tech skill 6): Must have experience with evaluation frameworks (precision, recall, F1, field-level accuracy) and iterative model improvement, plus AI observability (Prometheus, Grafana, SLOs).

10

Mandatory (Team Leadership): Must have experience leading projects or teams, managing technical deliverables, and mentoring, with strong problem-solving in ambiguous challenges

11

Mandatory (Education) - B.Tech or B.E. If graduated in BCA or BCS, they should have a Maters or Post graduation

12

Mandatory (Company): B2B Tech/Consulting/B2B Software