Lead I - Software Engineering - AI Solutions Analyst at Global Digital Transformation Solutions Provider · Bengaluru (Bangalore), Mumbai, Delhi, Gurugram, Noida, Ghaziabad, Faridabad, Pune, Hyderabad · 6 - 8 years · ₹10L - ₹26L / yr · Posted 30 Oct 2025
Lead I - Software Engineering - AI Solutions Analyst
at Global Digital Transformation Solutions Provider
Amazon Web Services (AWS)
ChatGPTMUST-HAVES:
- LLM, AI, Prompt Engineering LLM Integration & Prompt Engineering
- Context & Knowledge Base Design.
- Context & Knowledge Base Design.
- Experience running LLM evals
NOTICE PERIOD: Immediate – 30 Days
SKILLS: LLM, AI, PROMPT ENGINEERING
NICE TO HAVES:
Data Literacy & Modelling Awareness Familiarity with Databricks, AWS, and ChatGPT Environments
ROLE PROFICIENCY:
Role Scope / Deliverables:
- Scope of Role Serve as the link between business intelligence, data engineering, and AI application teams, ensuring the Large Language Model (LLM) interacts effectively with the modeled dataset.
- Define and curate the context and knowledge base that enables GPT to provide accurate, relevant, and compliant business insights.
- Collaborate with Data Analysts and System SMEs to identify, structure, and tag data elements that feed the LLM environment.
- Design, test, and refine prompt strategies and context frameworks that align GPT outputs with business objectives.
- Conduct evaluation and performance testing (evals) to validate LLM responses for accuracy, completeness, and relevance.
- Partner with IT and governance stakeholders to ensure secure, ethical, and controlled AI behavior within enterprise boundaries.
KEY DELIVERABLES:
- LLM Interaction Design Framework: Documentation of how GPT connects to the modeled dataset, including context injection, prompt templates, and retrieval logic.
- Knowledge Base Configuration: Curated and structured domain knowledge to enable precise and useful GPT responses (e.g., commercial definitions, data context, business rules).
- Evaluation Scripts & Test Results: Defined eval sets, scoring criteria, and output analysis to measure GPT accuracy and quality over time.
- Prompt Library & Usage Guidelines: Standardized prompts and design patterns to ensure consistent business interactions and outcomes.
- AI Performance Dashboard / Reporting: Visualizations or reports summarizing GPT response quality, usage trends, and continuous improvement metrics.
- Governance & Compliance Documentation: Inputs to data security, bias prevention, and responsible AI practices in collaboration with IT and compliance teams.
KEY SKILLS:
Technical & Analytical Skills:
- LLM Integration & Prompt Engineering – Understanding of how GPT models interact with structured and unstructured data to generate business-relevant insights.
- Context & Knowledge Base Design – Skilled in curating, structuring, and managing contextual data to optimize GPT accuracy and reliability.
- Evaluation & Testing Methods – Experience running LLM evals, defining scoring criteria, and assessing model quality across use cases.
- Data Literacy & Modeling Awareness – Familiar with relational and analytical data models to ensure alignment between data structures and AI responses.
- Familiarity with Databricks, AWS, and ChatGPT Environments – Capable of working in cloud-based analytics and AI environments for development, testing, and deployment.
- Scripting & Query Skills (e.g., SQL, Python) – Ability to extract, transform, and validate data for model training and evaluation workflows.
- Business & Collaboration Skills Cross-Functional Collaboration – Works effectively with business, data, and IT teams to align GPT capabilities with business objectives.
- Analytical Thinking & Problem Solving – Evaluates LLM outputs critically, identifies improvement opportunities, and translates findings into actionable refinements.
- Commercial Context Awareness – Understands how sales and marketing intelligence data should be represented and leveraged by GPT.
- Governance & Responsible AI Mindset – Applies enterprise AI standards for data security, privacy, and ethical use.
- Communication & Documentation – Clearly articulates AI logic, context structures, and testing results for both technical and non-technical audiences.
Shubham Vishwakarma
Full Stack Developer - Averlon
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About LeadSquared
LeadSquared is a leading sales execution and marketing automation platform trusted by 2,000+ businesses globally, including healthcare, education, financial services, and real estate. Headquartered in Bengaluru with offices across the US, UK, UAE, and Southeast Asia, we empower sales teams to close faster, smarter, and at scale.
Our AI team is at the forefront of integrating cutting-edge large language model capabilities into enterprise workflows — building intelligent agents, copilots, and automation systems that redefine how businesses operate.
Role Overview
We are looking for a Senior AI Engineer with hands-on experience building LLM-powered agents and agentic AI systems. You will design, develop, and deploy autonomous AI pipelines that solve complex, multi-step business problems — from lead qualification and follow-up automation to intelligent CRM workflows and beyond.
This role is ideal for someone who is deeply excited about the frontier of AI, can move fast, and wants their work to directly impact millions of sales professionals worldwide.
Key Responsibilities
•
Design and build LLM-powered agentic systems using frameworks such as LangChain, LlamaIndex, AutoGen, or CrewAI to automate complex, multi-step workflows.
•
Develop and maintain Retrieval-Augmented Generation (RAG) pipelines with vector databases (Pinecone, Weaviate, Chroma, pgvector) for domain-specific knowledge grounding.
•
Build and integrate tool-use and function-calling capabilities into AI agents, enabling dynamic interaction with internal APIs, databases, and third-party services.
•
Implement prompt engineering strategies including chain-of-thought, few-shot prompting, and structured output parsing to ensure reliable agent behavior.
•
Design evaluation frameworks and observability pipelines (LangSmith, Helicone, custom metrics) to monitor agent performance, accuracy, and cost.
•
Collaborate with product, sales, and domain teams to translate business requirements into AI-driven solutions and features.
•
Optimize LLM inference for latency and cost using techniques like caching, model distillation, quantization, and batching.
•
Stay current with the rapidly evolving LLM ecosystem and proactively propose improvements and new approaches.
•
Contribute to internal best practices, documentation, and knowledge-sharing across the engineering org.
Required Qualifications
Experience
•
2–4 years of professional software engineering experience, with at least 1–2 years focused on LLM/AI systems.
•
Proven experience shipping LLM-based products or agentic AI systems into production environments.
Technical Skills
•
Strong proficiency in Python and familiarity with async programming patterns for AI pipelines.
•
Hands-on experience with LLM APIs: OpenAI (GPT-4o), Anthropic (Claude), Google (Gemini), or open-source models (Llama, Mistral).
•
Experience with agentic frameworks: LangChain, LangGraph, LlamaIndex, AutoGen, CrewAI, or similar.
•
Solid understanding of RAG architectures, embedding models, and semantic search.
•
Experience with vector databases and similarity search infrastructure.
•
Knowledge of REST APIs, microservices architecture, and containerization (Docker/Kubernetes).
Problem-Solving & Mindset
•
Strong ability to decompose ambiguous, open-ended problems into structured AI system designs.
•
Experience with prompt debugging, LLM evaluation, and iterative refinement workflows.
•
Ability to balance research exploration with engineering pragmatism to ship reliable systems.
Preferred Qualifications
•
Experience with multi-agent orchestration and agent memory systems (short-term and long-term).
•
Familiarity with fine-tuning or RLHF workflows for domain adaptation.
•
Background in NLP, information retrieval, or conversational AI.
•
Prior experience in B2B SaaS or CRM domain is a plus.
•
Contributions to open-source AI/ML projects or published research/blogs.
•
Experience with cloud platforms: AWS, GCP, or Azure — particularly AI/ML services
Data Science
Machine Learning (ML)Strong AI Engineer / Machine Learning Engineer profiles.
2
Mandatory (Experience 1) – Must have minimum 3+ years of hands-on experience in Data Science, Machine Learning, Applied AI, NLP, Deep Learning, or Generative AI solutions.
3
Mandatory (Experience 2) – Must have strong hands-on experience in Python programming, SQL, data analysis, feature engineering, model development, and production-grade ML applications.
4
Mandatory (Experience 3) – Must have experience working with Machine Learning and Deep Learning frameworks such as PyTorch, TensorFlow, Keras, Scikit-learn, or equivalent.
5
Mandatory (Experience 4) – Must have hands-on experience working on NLP, embeddings, semantic search, text classification, document understanding, recommendation systems, or similar AI/ML use cases.
6
Mandatory (Experience 5) – Must have experience working with Large Language Models (LLMs) such as GPT, Llama, Mistral, Claude, Gemini, Phi, or similar foundation models.
7
Mandatory (Experience 6) – Must have hands-on experience building or implementing RAG (Retrieval Augmented Generation) systems, vector search, knowledge retrieval, embeddings, chunking, indexing, or semantic retrieval solutions.
8
Mandatory (Experience 7) – Must have experience working with Git, CI/CD practices, production environments, and scalable AI/ML systems.
9
Mandatory (CTC) – The CTC breakup offered will be 75% fixed + 25% variable, as per company policy.
10
Mandatory (Age) - Candidate's Age should be below 30 Years
11
Preferred (Experience 1) – Experience with MLFlow, Kubeflow, Airflow, Prefect, Feature Stores, Model Registry, or MLOps/LLMOps frameworks.
12
Preferred (Experience 2) – Experience working with Vector Databases, Spark, PySpark, distributed ML pipelines, large-scale data processing, or real-time ML systems..
13
Preferred (Experience 3) – Familiarity with Docker, Kubernetes, Azure, AWS, GCP, cloud-native AI deployments, and scalable ML architecture.
14
Preferred (Company) – Candidates from AI-first startups, Fintech, Banking, Lending, Fraud Analytics, Risk Analytics, Product Companies, SaaS organizations, or data-driven technology companies
15
Mandatory ( Pedigree) - B.TECH / M.TECH from Tier 1 Colleges (IIT's, NIT's, BITS) are Considered.
PythonMachine Learning (ML)The Role
You own AI systems end to end. From the speech-to-text models that turn audio into text, to the diarization that separates and identifies speakers, to the agentic layer that turns conversation into memory and action, to the observability and evaluation that keep all of it honest in production. This is a wide role by design. You will own model selection, serving, and production reliability. If you want to tune one model and ignore the system around it, this is not the role.
What You Will Own
• Speech-to-text. Evaluate, integrate, and optimize STT models across cloud and self-hosted. Drive accuracy and cost trade-offs with ground-truth metrics.
• Speaker diarization and identification. Push accuracy on hard, real-world, multi-speaker audio.
• Agentic AI. Build the memory and retrieval pipeline, LLM orchestration, and the agent workflows that sit on top of captured conversation.
• Model serving and infrastructure. Stand up and optimize self-hosted serving (vLLM, Triton class). Own latency, throughput, and cost per user.
Observability
An always-on wearable means models run in production every second, on messy real-world audio. You own the visibility into that.
• Instrument the full audio-to-memory pipeline: STT, diarization, retrieval, and LLM calls.
• Define and track model-quality SLOs in production: transcription drift, diarization error over time, retrieval relevance, latency, throughput, and cost per user.
• Build dashboards and alerting so model degradation is caught before users feel it.
• Trace failures across a distributed, always-on system using metrics, logs, and traces.
• Close the loop. Production signals feed back into evaluation and model selection.
Evaluation
We do not ship what we cannot measure. You own the systems that prove a model is actually better, not just newer.
• Build and own ground-truth evaluation harnesses for every model in the stack.
• Measure with real metrics: WER for transcription, DER for diarization, Recall and F1 for retrieval and speaker identification.
• Build and maintain labeled benchmark datasets that reflect real, messy, multi-speaker audio.
• Run regression and A/B evaluations on every model swap, prompt change, or pipeline update. Nothing ships on a vibe.
• Reject anecdotal proxies, single confidence scores, and cherry-picked examples as evidence of quality.
What We Are Looking For
• 3 to 5 years as an AI/ML engineer with production systems behind you. Engineering and production experience is non-negotiable.
• Depth across the modern AI stack: LLMs, speech models, vector retrieval, model serving.
• Strong software engineering. You write code that ships and survives contact with real users.
• Fluency in Python and the production ML ecosystem.
• Comfort with cloud infrastructure (GCP a plus) and containerized deployment on Kubernetes.
• A working command of observability and evaluation. You measure first and trust metrics over intuition.
• First-principles reasoning and metric discipline.
Nice to Have
• Research background or publications. A strong signal, not a substitute for production work.
• Audio and speech ML experience (STT, diarization, voice).
• Experience self-hosting and optimizing open models.
• Experience with LLM gateway and agent orchestration patterns.
• Experience building eval harnesses or production model-monitoring systems.
Requirements
Agentic work is must. Audio is good to have
. Self hosting models is a must
Experience with LLM gateway and agent orchestration is a must have
PythonSenior AI Engineer
Code Generation, Agent Architecture & LLM Systems
📍 Mumbai (On-site) | Full-time | 5+ years
About the Role:
Unico Connect is an AI-first technology partner that builds custom mobile, web, and AI products for clients across multiple geographies.
We are hiring a Senior AI Engineer for a dedicated client engagement focused on building an AI-powered application builder platform - a product where users describe software in plain English and the system generates, previews, and iteratively refines working code.
The mandatory requirement for this role is hands-on production experience shipping LLM-powered systems with agent architectures, with experience in code generation or developer tooling contexts a strong advantage.
The role is product-focused and deeply hands-on. You will own everything between the user's prompt and correct code landing in the project: the agentic loop, code generation pipeline, context management, evaluation suite, and model cost strategy.
You will work alongside the Senior MLOps Engineer who operationalises the infrastructure around your system, and collaborate closely with backend, frontend, and DevOps engineers.
Responsibilities:
Agent Architecture
Design and own the agentic loop for the platform - request interpretation, planning, tool-calling sequence (read file, edit file, run build, search code, install package), and stop conditions.
Make and revisit architectural decisions on single-agent vs. multi-agent designs, including planner/executor splits and dedicated build-repair sub-agents.
Code Generation Pipeline
Own the end-to-end generation flow: task classification, context gathering, planning, targeted edits, verification, and commit.
Implement diff/search-replace-based file editing with fuzzy matching and fallback strategies.
Enforce scope discipline so the agent makes minimal diffs and does not modify code it was not asked to touch.
Self-Repair Loop
Build and tune the automated repair loop that pipes compiler, lint, build, and runtime errors back to the model with retry budgets and model escalation.
This loop is the primary quality lever - the difference between 60-70% and 90%+ build success rates.
Context Management
Build file-relevance retrieval so the agent sees the right files, not the whole codebase: dependency graphs, AST/tree-sitter-based chunking, embeddings, recency signals, and hybrid retrieval.
Implement conversation summarisation and memory for long sessions, and address long-project degradation through codebase summaries and periodic consistency passes.
Own token budgeting and prompt caching strategy.
Prompt Engineering as a Discipline
Own the system prompt and per-task prompt variants (new feature, bug fix, styling change).
Maintain few-shot examples and enforce coding conventions, stack rules, and prohibited behaviours such as no hardcoded secrets and no whole-file rewrites.
Version prompts like code with changelogs and rollback capability.
Evaluation and Quality Measurement
Design and own the evaluation suite: representative test prompts run on every prompt and model change, scored on build success rate, instruction adherence, and output quality including LLM-as-judge and visual/screenshot checks where relevant.
Define regression gates that block quality-degrading changes from shipping.
Treat evals the way engineers treat automated testing: versioned, automated, and tracked over time.
This responsibility is non-negotiable at this level.
Model Strategy and Cost
Design model routing - cheap and fast models for classification and small edits, frontier models for complex generation.
Drive cost optimisation through prompt caching, diff-based edits over full-file rewrites, and tighter context selection.
Track cost per agent run and tokens per task; evaluate new model releases against the eval suite and lead migrations when results justify it.
Safety and Reliability of Agent Behaviour
Defend against prompt injection from user content and fetched web content.
Ensure secrets never appear in generated client code.
Define what the agent's tools may and may not do in collaboration with the platform team.
Contribute to output moderation and abuse-pattern awareness.
Mentorship and Engineering Standards
Run code reviews, define engineering conventions for AI work, and raise the engineering bar across the AI team.
Work closely with the Senior MLOps Engineer on handoff of eval design, prompt configurations, and model routing logic.
Requirements:
Hands-on Production Ownership of LLM-Powered Systems with Agent Architectures (Mandatory)
Must have personally shipped and operated at least one complex production AI system - agentic, multi-step, or code generation - with end-to-end ownership of architecture, evaluation, and cost.
POCs, internal demos, and tutorial-grade work do not qualify.
5+ Years of Professional Software or AI Engineering Experience
With at least 3 years focused on LLM applications, AI engineering, or production AI systems.
Candidates with strong backend backgrounds and a clear, substantive pivot into LLM systems qualify.
Strong Python Proficiency and Service Development
Production-grade Python with FastAPI or equivalent: type hints, async patterns, streaming responses, testing, and packaging.
Not notebook-only.
Depth Across LLM APIs and Agent Systems
Production experience with at least two of OpenAI, Anthropic Claude, Google Gemini, or open-weight models (vLLM, Ollama, Together).
Production experience with at least one agent framework (LangGraph, CrewAI, AutoGen, LlamaIndex Agents) or hand-rolled equivalent.
Hands-on with tool calling, structured outputs, and multi-step reasoning.
Demonstrated, Systematic Evaluation Practice - Non-Negotiable
Must have built evaluation harnesses that gate production releases, not ad-hoc testing.
Hands-on with at least one of LangSmith, Langfuse, Promptfoo, Ragas, or DeepEval.
Candidates with no systematic answer to evaluation should not be considered at senior level regardless of other strengths.
Cost Discipline for Production AI
Track record of measurable cost optimisation on production AI features.
Able to speak in specifics: cost per request, savings achieved through caching or model routing, context reduction decisions.
AWS Working Knowledge
Hands-on with EC2, S3, IAM, and Docker.
Comfort with CI/CD workflows and deploying AI services.
Awareness of LLM Security Failure Modes
Familiar with prompt injection patterns, understands that system prompt rules alone are insufficient, and has experience with output validation and content safety in production.
Nice to Have
- Experience with AST/tree-sitter tooling, diff-based editing systems, or compiler-adjacent work
- MCP server authoring
- Open-source AI contributions
- Published technical writing on LLM systems
- Multi-modal model experience
- Fine-tuning exposure (LoRA, QLoRA, PEFT)
PythonSkill Set
Large language,Artificial Intelligence,Machine Learning
- 4–7 years of experience in software engineering/AI roles
- Strong programming skills in Python or TypeScript (Java/Go is a plus)
- Hands-on experience with LLMs, RAG pipelines, and AI frameworks
- Experience building APIs and working with distributed systems
- Familiarity with Kubernetes, Docker, and CI/CD pipelines
- Experience with cloud platforms (AWS/Azure/GCP)
Excellent communication
PythonWhat We Are Looking For
CLOUDSUFI is seeking a senior, hands-on AI Platform Architect to design and build production-grade platforms for generative AI, agentic systems, data-intensive applications, and analytical workflows. This is a builder-architect role. The successful candidate will define architecture, make technology decisions, develop reference implementations, review critical code and designs, and guide engineering teams from prototypes to secure, scalable production systems. We are looking for a builder-architect with strong engineering judgement and practical delivery experience. The right candidate can define platform direction, evaluate trade-offs, validate ideas through implementation, and guide systems into production. They should be equally comfortable discussing distributed architecture, reviewing code, diagnosing workflow failures, designing evaluation systems, and mentoring engineering teams.
Key Responsibilities-
AI and Agentic Platform Architecture
• Design platforms for single-agent and multi-agent systems supporting planning, reasoning, tool use, memory, delegation, validation, and human approval.
• Define orchestration patterns for deterministic, dynamic, event-driven, and long-running AI workflows.
• Establish clear boundaries between LLM reasoning, application logic, quantitative computation, rules, and human decision-making.
• Evaluate and adopt agent frameworks, model providers, tools, and orchestration technologies based on reliability, flexibility, performance, and cost. Knowledge and Data Systems
• Architect RAG pipelines, document-processing systems, vector search, hybrid retrieval, knowledge graphs, and semantic data layers.
• Integrate structured and unstructured enterprise data from APIs, databases, files, streams, and external platforms.
• Design reusable workflows for research, data collection, transformation, analysis, modelling, validation, and reporting.
• Establish data lineage, provenance, metadata, access controls, freshness, and quality standards. Evaluation, Observability and Governance
• Build evaluation frameworks for accuracy, relevance, groundedness, task completion, tool use, safety, latency, and cost.
• Enable systematic experimentation across models, prompts, agents, tools, retrieval strategies, and orchestration patterns.
• Implement versioning and lifecycle management for prompts, agents, workflows, datasets, knowledge bases, evaluations, and model configurations.
• Establish tracing, monitoring, auditability, guardrails, approval workflows, and production quality diagnostics.
Cloud and Platform Engineering
• Define cloud-native architectures using microservices, APIs, event-driven systems, queues, schedulers, and distributed processing.
• Lead Kubernetes-based deployment, containerisation, CI/CD, Infrastructure as Code, environment management, and release automation.
• Design for horizontal scalability, fault tolerance, resilience, security, data privacy, and high availability.
• Optimise model usage, infrastructure, storage, retrieval, and compute for performance, latency, and cost.
Technical Leadership
• Translate product and business requirements into clear technical designs and implementation plans.
• Build prototypes and reference implementations for high-risk or foundational platform capabilities.
• Review architecture, code, interfaces, data models, infrastructure, and operational readiness.
• Define engineering standards and reusable patterns across AI, backend, data, and platform teams.
• Mentor senior engineers and support teams in resolving complex technical and production issues.
Required Skills and Experience
• 10+ years of experience in software architecture, platform engineering, distributed systems, data platforms, or AI systems.
• Strong hands-on experience designing and building production-grade AI or data-intensive platforms.
• Deep understanding of LLM applications, tool calling, structured outputs, RAG, embeddings, memory, and agent orchestration.
• Strong experience with cloud platforms, Kubernetes, containers, microservices, APIs, event driven architecture, CI/CD, and Infrastructure as Code.
• Experience with relational, document, graph, vector, and distributed data systems.
• Practical experience implementing AI evaluation, experimentation, tracing, monitoring, guardrails, and lifecycle management.
• Strong understanding of security, identity, access control, secrets management, data protection, and production reliability.
• Ability to move effectively between architecture, code, infrastructure, debugging, and technical delivery.
Good to Have
• Experience building enterprise AI copilots, autonomous workflows, research platforms, or analytical systems.
• Experience with knowledge graphs, hybrid search, model gateways, tool gateways, or agent marketplaces.
• Familiarity with LLMOps, MLOps, model serving, feature stores, model registries, and distributed compute.
• Experience supporting real-time and batch data processing at scale.
• Experience comparing and operating multiple commercial and open-source models.
• Prior experience in consulting, client-facing architecture, or complex enterprise platform delivery.
Python
JavascriptLocation: Hyderabad, India (home base), deployed at client sites in India. Occasional Middle East exposure possible.
About the Role
You will work as a senior AI engineer who embeds inside a customer's business. Your job is to learn how the business makes money, find the highest value problem, and build a working system that solves it.
Four behaviors define this role:
- Go where the work happens. You work onsite with the customer, in the room where decisions are made.
- Show working software early. You build a prototype in days, not a document in weeks.
- One person owns the outcome. You are the single point of accountability for the result.
- Stay after go-live. You keep running and improving the system after launch.
You are the single point of accountability. You are not a solo builder. A full KnackLabs engineering team in Hyderabad builds and runs the production systems behind you.
This role involves extended onsite deployments at client locations in other cities, sometimes up to six months at a stretch. Please apply only if you are ready for this way of working.
What you'll own
- Discovery - Learn how the customer makes money. Find the highest value problem to solve first.
- The prototype - Build a working prototype fast, using real or sample data, to prove the idea.
- The roadmap - Decide what to build, in what order, and set clear success measures tied to business outcomes.
- The build - Design and ship the production system with the Hyderabad engineering team. This includes data integration, agents, retrieval, and evaluations.
- The client relationship - Be the trusted technical contact for the customer, from engineers to senior leaders.
- Go live and after - Deploy the system, watch how it performs, fix problems, and improve it over time.
- Feedback to the product - Share what you learn in the field so the vendor's product and our internal tools get better.
What we are looking for
- Around 4 or more years of software engineering experience, including customer-facing or client delivery work.
- Strong programming skills in Python. Working knowledge of TypeScript or JavaScript.
- A full-stack development experience with strength in backend technologies.
- Production experience with large language models, including prompt engineering and agent development.
- You build with AI coding tools like Claude Code or Codex as your default way of working, and you have shipped real apps or agents this way.
- Experience building retrieval-augmented generation (RAG) systems: chunking, embeddings, vector databases, retrieval, and reranking.
- Experience building and deploying AI systems.
- Experience integrating with APIs and enterprise systems.
- Experience with at least one cloud platform (AWS, Azure, or GCP).
- Clear communication. You can explain a technical choice to an engineer and to a business leader.
- High ownership and comfort with ambiguity. You can take an unclear problem and turn it into a plan.
- Willingness to work onsite at client locations in India for extended periods, and to travel as the work needs.
Nice to have
- Experience with on-premises or private cloud (VPC) deployments.
- Experience with observability and tracing tools such as LangSmith or Braintrust.
- Experience with data engineering and pipelines.
- A history of side projects, open source contributions, or products you shipped end-to-end.
- Experience in embedded or forward-deployed roles before.
- Experience working at a consulting or professional services firm in a client-facing delivery role.
Stack and tools
- Languages: Python and TypeScript.
- Models: Claude and other frontier or open-source models, chosen to fit the customer.
- AI patterns: RAG, agents, prompt engineering, and evaluations.
- Vector and retrieval: vector databases and retrieval pipelines.
- Cloud: AWS, Azure, or GCP, on public or private cloud.
- Integration: REST APIs and enterprise system connectors.
Strong AI Engineer / Machine Learning Engineer profiles.
2
Mandatory (Experience 1) – Must have minimum 3+ years of hands-on experience in Data Science, Machine Learning, Applied AI, NLP, Deep Learning, or Generative AI solutions.
3
Mandatory (Experience 2) – Must have strong hands-on experience in Python programming, SQL, data analysis, feature engineering, model development, and production-grade ML applications.
4
Mandatory (Experience 3) – Must have experience working with Machine Learning and Deep Learning frameworks such as PyTorch, TensorFlow, Keras, Scikit-learn, or equivalent.
5
Mandatory (Experience 4) – Must have hands-on experience working on NLP, embeddings, semantic search, text classification, document understanding, recommendation systems, or similar AI/ML use cases.
6
Mandatory (Experience 5) – Must have experience working with Large Language Models (LLMs) such as GPT, Llama, Mistral, Claude, Gemini, Phi, or similar foundation models.
7
Mandatory (Experience 6) – Must have hands-on experience building or implementing RAG (Retrieval Augmented Generation) systems, vector search, knowledge retrieval, embeddings, chunking, indexing, or semantic retrieval solutions.
8
Mandatory (Experience 7) – Must have experience working with Git, CI/CD practices, production environments, and scalable AI/ML systems.
9
Mandatory (CTC) – The CTC breakup offered will be 75% fixed + 25% variable, as per company policy.
10
Mandatory (Age) - Candidate's Age should be below 28 Years
Machine Learning (ML)PythonStrong AI Engineer / Machine Learning Engineer profiles.
2
Mandatory (Experience 1) – Must have minimum 5+ years of hands-on experience in Data Science, Machine Learning, Applied AI, NLP, Deep Learning, or Generative AI solutions.
3
Mandatory (Experience 2) – Must have strong hands-on experience in Python programming, SQL, data analysis, feature engineering, model development, and production-grade ML applications.
4
Mandatory (Experience 3) – Must have experience working with Machine Learning and Deep Learning frameworks such as PyTorch, TensorFlow, Keras, Scikit-learn, or equivalent.
5
Mandatory (Experience 4) – Must have hands-on experience working on NLP, embeddings, semantic search, text classification, document understanding, recommendation systems, or similar AI/ML use cases.
6
Mandatory (Experience 5) – Must have experience working with Large Language Models (LLMs) such as GPT, Llama, Mistral, Claude, Gemini, Phi, or similar foundation models.
7
Mandatory (Experience 6) – Must have hands-on experience building or implementing RAG (Retrieval Augmented Generation) systems, vector search, knowledge retrieval, embeddings, chunking, indexing, or semantic retrieval solutions.
8
Mandatory (Experience 7) – Must have experience working with Git, CI/CD practices, production environments, and scalable AI/ML systems.
9
Mandatory (CTC) – The CTC breakup offered will be 75% fixed + 25% variable, as per company policy.
10
Mandatory (Age) - Candidate's Age should be below 30 Years
11
Preferred (Experience 1) – Experience with MLFlow, Kubeflow, Airflow, Prefect, Feature Stores, Model Registry, or MLOps/LLMOps frameworks.
12
Preferred (Experience 2) – Experience working with Vector Databases, Spark, PySpark, distributed ML pipelines, large-scale data processing, or real-time ML systems..
13
Preferred (Experience 3) – Familiarity with Docker, Kubernetes, Azure, AWS, GCP, cloud-native AI deployments, and scalable ML architecture.
14
Preferred (Company) – Candidates from AI-first startups, Fintech, Banking, Lending, Fraud Analytics, Risk Analytics, Product Companies, SaaS organizations, or data-driven technology companies
15
Mandatory ( Pedigree) - B.TECH / M.TECH from Tier 1 Colleges (IIT's, NIT's, BITS) are Considered.
About the role
We are seeking an AI Engineer to build and implement AI systems for content production at scale. You'll work at the intersection of engineering and content designing prompt pipelines, integrating generative models, and building the tooling that turns source material into finished creative output. The ideal candidate is technically strong but also has taste: someone who understands story and craft, and can tell the difference between output that's technically correct and output that's actually good.
Responsibilities
- Build and iterate on prompt pipelines and multi-agent workflow components
- Design and integrate agentic workflows orchestrate multi-step, tool-using agents that plan, call models, and hand off between stages in production
- Deploy and serve open-source models set up inference endpoints, manage GPU compute, and optimize for latency and cost
- Write evals compare outputs against references, quantify quality, and feed results back into the pipeline
- Work on data pipelines: structured extraction from messy source text, localization, similarity/dedup
- Debug and maintain pipeline stages in production
What you bring:
- (1+/3+) years of engineering experience, or a strong portfolio of shipped projects
- Solid Python fundamentals clean, working, readable code
- Hands-on experience with LLM APIs and prompt engineering (personal projects count)
- Comfort with Git, REST APIs, and working in a Linux environment
- A feel for content and narrative you can judge whether generated output is actually good, not just valid
- Curiosity and clear communication you ask good questions and don't stay stuck silently
Preferred
- Exposure to agent/orchestration frameworks (LangGraph, LangChain, CrewAI)
- Familiarity with vector databases, embeddings, or RAG (Qdrant, pgvector)
- Hands-on work with open-source generative media models Flux, LTX, Wan, or similar
- Experience deploying open-source models for inference (vLLM, ComfyUI, Replicate/Cog, Docker + GPU)
- Experience writing evals or LLM-as-judge scoring
- Node.js and Fastapi familiarity, or experience deploying on AWS
Follow CutshortShubham Vishwakarma
Full Stack Developer - Averlon