Data and AI practice

About E2M:

E2M Solutions works as a trusted white-label partner for digital agencies. We support agencies with consistent and reliable delivery through services such as website design, web development, eCommerce, SEO, AI SEO, PPC, AI automation, and content writing .Founded on strong business ethics, we are an equal opportunity organization powered by 300+ experienced professionals, partnering with 400+ digital agencies across the US, UK, Canada, Europe, and Australia. At E2M, we value ownership, consistency, and people who are committed to doing meaningful work and growing together .If you’re someone who dreams big and has the gumption to make them come true, E2M has a place for you.

Role Overview:

We are seeking a highly skilled and client-centric AI Consultant/AI Adoption Specialist to join our growing team. In this pivotal role, you'll serve as a vital link between our clients' strategic objectives and the transformative power of AI. You'll primarily focus on understanding their needs, scoping opportunities, and architecting actionable AI roadmaps.

Key Responsibilities:

• Collaborate closely with clients to understand their challenges and identify opportunities to apply AI.
• Assess client requirements and prepare solution strategies using AI tools and methodologies.
• Work with internal teams to design, propose, and help execute AI-powered solutions.
• Provide AI-based recommendations that align with the client’s business objectives.
• Communicate technical possibilities in a business-friendly manner to decision-makers.
• Take ownership of the client journey from discovery to implementation and support.
• Stay updated with AI trends, tools, and real-world use cases that can benefit clients.

Required Skills & Qualifications:

• Minimum 2+ Years of hands on experience into Custom AI Development.
• Minimum 3+ years of experience in roles like Project Manager, Customer Success Manager, or Account Manager, preferably in a service-based company or digital agency.
• Strong understanding of AI concepts, trends, and tools (e.g., NLP, ML, Chatbots, Automation, native cloud technologies).
•  Some hands-on experience in AI projects – either through execution, coordination, or implementation.
• Ability to manage multiple client engagements and communicate effectively with both technical and non-technical stakeholders.
•  Strong problem-solving mind set with the ability to translate business needs into AI opportunities.
• Flexible to work with international clients, especially in the US time zone as needed.

The role

A working product and a working deployment are two different things. You are the person who closes that gap.

You sit inside the client's head office. You get the deployment live, you get their teams using the dashboards, and you own whether the AI is returning something worth acting on. Every client is different: different languages on the floor, different store noise, different vocabulary for the same product, different CRM, different idea of what a good conversation looks like. The core platform does not change for each of them. You are the layer that makes it fit, and you are the one the client meets.

You are encouraged to spend time in stores. The engineers who do the best work here are the ones who have stood on a shop floor and watched where the pitch and the pipeline actually break. Nobody will make you go. You will also spend real time in the codebase, because you fix what you find rather than filing it.

What you build has a commercial edge to it. A pilot converts when the client sees the result they were promised, and an account grows when a second team inside it sees what is already sitting in their data. Both of those outcomes are yours to deliver, not somebody else's to chase.

How you will work

You design the deployment, you build it, and you own whether it holds up on a Saturday evening in a crowded store. Nobody hands you the plan, and nobody hands you the spec. You write both.

The decisions are yours. Which integration is worth the week, which vertical taxonomy needs building, what ships in the pilot and what waits, and when to tell a client that the thing they are asking for is the wrong thing to build. You go and find out what a client needs before anyone writes a line of code.

You will not be doing it alone. There are founders, AI engineers and product people around you, and they will build alongside you. What nobody will do is tell you what the client needs. That call is yours.

The work compounds if you do it well. What you learn on one deployment becomes a specification, then code, then a pattern the next one starts from. A year in, the deployments you designed should be running without you, and a new client should take a fraction of the time the first one did.

What you will do

Own the deployment end to end. Device provisioning, store connectivity, data flowing, first insight in front of the client. Get from kickoff to something real inside

the pilot window, and know by the halfway mark whether it is in trouble.

Get their HQ using it. A dashboard nobody opens is a failed deployment. Sit with the sales, marketing and L&D teams, show them what is in their own data, and

make sure the people who asked for this are actually looking at it every week.

Make the AI work on their floor. Their languages, their store noise, their product vocabulary. Benchmark transcription and speaker separation on their actual

audio, and fix what fails instead of explaining it away.

Build the vertical. Intent taxonomies, objection maps and prompt libraries for the category you are deployed into. A jewellery floor and an electronics floor do not

share a conversation model.

Wire it into their systems. CRM and POS integrations, so conversation data connects to what actually got sold and the insight can be checked against reality.

Build what the client asks for. Custom reports, dashboards and agents. Ground everything in source conversations and verify it before it ships, because a confident

wrong number costs an account.

Close the pilot. A pilot converts on results, not on effort. Know what the client agreed to judge this on, work backwards from it, and make sure the output in front of

their leadership at the end is the thing they asked for.

Grow the account. The same intelligence is worth something to marketing, L&D and category teams inside the same client. Spot which of them would benefit, show them what is already in their data, and hand a real opening to the account team.

Push it back into the product. Turn one-off client work into something the platform does by default, so the next deployment starts further ahead than this one did.

What we are looking for

Must have

• 0 to 5 years of experience. A consulting internship or an analyst role is the closest match to what this job actually asks for, but we care more about what you can do than where you did it
• Coding ability, ideally Python. Degree, internship, first job or your own projects. What we want to see is something you built that other people actually used
• Excel or Sheets at a real working level. A lot of the first conversation with a client happens in a spreadsheet before it ever happens in a dashboard
• The ability to explain a complicated idea simply. You will be taking AI output to people who do not think about models, and the explanation matters as much as the result
• Comfort at the boundaries. APIs, data pipelines, some frontend, some hardware when a device misbehaves
• An eye for where a deployment turns into more business, and the willingness to raise it yourself
• Heavy hands-on LLM usage. Prompts, evaluations, retrieval, and a clear view on where these tools break
• Fluent English and Hindi. A third Indian language counts for a lot, since the useful conversations happen on store floors and not only in HQ meeting rooms
• The instinct to go and find out what a client needs rather than waiting to be told

Good to have

Speech or audio work. Transcription, diarization, voice activity detection, or

anything that survives noisy real-world recording

Embedded or IoT experience, on ESP32 or similar

SQL and experience building things customers actually look at

Side projects, hackathons or internships where you shipped without a spec

and it worked

🚀 We’re Hiring | Data Scientist 🧠📊

Ready to turn data into real-world intelligence? Join us and work on exciting AI/ML & data-driven solutions!

🔹 Experience: 8+ Years

🔹 Must-Have Skills:

🐍 Python | 🤖 Machine Learning | ☁️ Cloud | 🧠 NLP | 📊 Data Visualization

📍 Location: Pune

💼 Work Mode: Work from Office

If you're passionate about Data Science, AI & solving complex business problems, we’d love to hear from you!

📩 Interested? Kindly text

#Hiring #DataScientist #DataScience #MachineLearning #Python #NLP #AI #Cloud #DataVisualization #TechJobs #HiringNow

Location: 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 consultant 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.

You will not hand over a document and walk away. You will show working software early, own the roadmap, own the client relationship, and stay after go-live to run and improve the system.

Four behaviors define this role:

1. Go where the work happens. You work onsite with the customer, in the room where decisions are made.
2. Show working software early. You build a prototype in days, not a document in weeks.
3. One person owns the outcome. You are the single point of accountability for the result.
4. 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

1. Discovery - Learn how the customer makes money. Find the highest value problem to solve first.
2. The prototype - Build a working prototype fast, using real or sample data, to prove the idea.
3. The roadmap - Decide what to build, in what order, and set clear success measures tied to business outcomes.
4. The build - Design and ship the production system with the Hyderabad engineering team. This includes data integration, agents, retrieval, and evaluations.
5. The client relationship - Be the trusted technical contact for the customer, from engineers to senior leaders.
6. Go live and after - Deploy the system, watch how it performs, fix problems, and improve it over time.
7. Feedback to the product - Share what you learn in the field so our platform and internal tools get better.

What we are looking for

1. Around 7 or more years of software engineering experience, including customer-facing or client delivery work.
2. Experience working at a consulting or professional services firm in a client-facing delivery role.
3. A full-stack development experience with strength in backend technologies.
4. Strong programming skills in Python. Working knowledge of TypeScript or JavaScript.
5. Production experience with large language models, including prompt engineering and agent development.
6. You build with AI coding tools like Claude Code as your default way of working. You have built real apps and agents this way, not just used it for document generation or review.
7. Experience building retrieval-augmented generation (RAG) systems: chunking, embeddings, vector databases, retrieval, and reranking.
8. Experience building and deploying AI systems.
9. Experience integrating with APIs and enterprise systems.
10. Experience with at least one cloud platform (AWS, Azure, or GCP).
11. Experience building evaluations to measure accuracy, safety, latency, and cost.
12. Clear communication. You can explain a technical choice to an engineer and to a business leader.
13. High ownership and comfort with ambiguity. You can take an unclear problem and turn it into a plan.
14. Willingness to work onsite at client locations in India for extended periods, and to travel as the work needs.

Nice to have

1. Experience deploying AI systems in regulated industries such as insurance, banking, or the public sector.
2. Experience with on-premises or private cloud (VPC) deployments.
3. Experience with observability and tracing tools such as LangSmith or Braintrust.
4. Experience with data engineering and pipelines.
5. A history of side projects, open source contributions, or products you shipped end-to-end.
6. Experience in embedded or forward-deployed roles before.

Stack and tools

1. Languages: Python and TypeScript.
2. Models: Claude and other frontier or open source models, chosen to fit the customer.
3. AI patterns: RAG, agents, prompt engineering, and evaluations.
4. Vector and retrieval: vector databases and retrieval pipelines.
5. Cloud: AWS, Azure, or GCP, on public or private cloud.
6. Integration: REST APIs and enterprise system connectors.

Role Overview 

As a Data Scientist, you will work with business stakeholders, AI engineers, and domain experts to transform data into actionable insights and intelligent solutions. You will develop machine learning models, perform statistical analysis, and contribute to AI-driven products that create measurable business impact. 

Key Responsibilities 

Data Science & Machine Learning 

• Analyze structured and unstructured data to identify patterns, trends, and business opportunities.  
• Perform exploratory data analysis (EDA), feature engineering, and data preparation.  
• Develop, evaluate, and optimize machine learning models for prediction, classification, clustering, and forecasting.  
• Apply statistical techniques to solve business problems and validate model performance.  
• Design and execute experiments to improve model accuracy and business outcomes.  

 AI Solution Development 

• Collaborate with AI Engineers, Data Engineers, and domain experts to build AI-powered solutions.  
• Translate business requirements into scalable data science approaches.  
• Contribute to Generative AI and advanced analytics initiatives where applicable.  
• Document methodologies, model performance, and key findings.  

 Required Technical Skills 

• Strong programming skills in Python and SQL for data analysis, feature engineering, and machine learning.  
• Strong understanding of Statistics, Probability, Linear Algebra, and Calculus as applied to machine learning and data science.  
• Experience with Exploratory Data Analysis (EDA), data preprocessing, feature engineering, feature selection, and handling missing or imbalanced data.  
• Good understanding of Supervised, Unsupervised, and Ensemble Machine Learning algorithms, including their assumptions, strengths, limitations, and appropriate use cases.  
• Strong knowledge of Regression, Classification, Clustering, Time Series Forecasting, Dimensionality Reduction, Recommendation Systems, and Anomaly Detection techniques.  
• Experience with Model Evaluation, Cross-Validation, Hyperparameter Optimization, Bias-Variance Trade-off, Feature Importance, Explainable AI (XAI), and Performance Metrics.  
• Understanding of Statistical Inference, Hypothesis Testing, Probability Distributions, Sampling Techniques, Confidence Intervals, and A/B Testing.  
• Experience translating business problems into analytical approaches and developing scalable, data-driven solutions.  
• Working knowledge of Generative AI, Large Language Models (LLMs), Prompt Engineering, and Retrieval-Augmented Generation (RAG) is preferred. 

Preferred Qualifications 

• Bachelor's or master's degree in computer science, Artificial Intelligence, Data Science, Statistics, Mathematics, Engineering, or a related field.  
• 2–4 years of experience developing machine learning or data science solutions.  
• Experience working on end-to-end data science projects in a business environment.  

 Nice to Have 

• Exposure to Generative AI, LLMs, RAG, or Agentic AI.  
• Experience with Computer Vision or Natural Language Processing (NLP).  
• Familiarity with cloud-based AI platforms.  
• Knowledge of construction, engineering, manufacturing, or industrial domains.  
• Participation in hackathons, research, Kaggle competitions, or open-source projects.  

 Soft Skills 

Strong analytical and problem-solving skills, effective communication and collaboration, ownership mindset, adaptability, continuous learning, and a passion for innovation.

Role Overview 

The Business Analyst for the CCX Customer Success AI Acceleration workstream partners closely with Engineering, Product Management, and Program teams to translate customer success strategy and AIdriven use cases into clear, actionable requirements that enable scalable delivery. This role sits on the engineering side of the house, acting as a connective layer between business intent, technical execution, and AIenabled platform outcomes. 

The Business Analyst ensures that AI acceleration initiatives across the Customer Success Platform are well‑defined, appropriately prioritized, and traceable from business objectives through system design and delivery. This includes refining problem statements, documenting requirements, supporting backlog readiness, and helping teams move quickly without losing alignment or clarity. 

Requirements 

• Proven experience as a Business Analyst supporting engineering teams, preferably in enterprise, platform, or SaaS environments. 

• Strong ability to translate business needs, customer success objectives, and AI use cases into clear functional and non‑functional requirements. 

• Experience working on AI‑enabled or data‑driven products, including concepts such as automation, recommendations, agentic workflows, or insights powered by analytics. 

• Ability to partner effectively with Engineers, Product Managers, Technical Program Managers, and UX to support backlog refinement and delivery planning. 

• Experience creating and maintaining artifacts such as user stories, acceptance criteria, process flows, data mappings, and dependency documentation. 

• Comfort operating in fast‑paced, iterative delivery environments, supporting AI acceleration efforts where requirements evolve quickly. 

• Strong analytical skills with the ability to identify gaps, risks, dependencies, and edge cases early in the development lifecycle. 

• Clear written and verbal communication skills, with the ability to align technical and non‑technical stakeholders. 

• Familiarity with ServiceNow‑style enterprise platforms, customer success workflows, or complex system integrations is a strong plus. 

• Bachelor’s degree in Business, Information Systems, Engineering, or a related field—or equivalent professional experience. 

Hiring for Data Scientist / Senior Data Scientist

Exp : 4 - 12 yrs

Edu : BE/B.tech/MCA

Work Location : Pune

Notice Period : Immediate - 15 days

Skills :

4+ years of experience in data engineering, data science, or related domains.

Hands-on experience with SQL, Python, and distributed data systems.

Knowledge of machine learning techniques and statistical analysis.

Experience with cloud data platforms (Azure Data Factory, AWS Glue, GCP BigQuery).

Familiarity with DevOps practices and CI/CD for data pipelines.

Platforms & Operations Experience (Preferred)

- Experience working with Azure, AWS, or Google Cloud data tools.

Operational experience with data orchestration tools (Airflow, ADF, Glue).

Understanding of Kubernetes, Docker, or containerized environments.

Hands-on experience with data warehousing platforms (Snowflake, Redshift, BigQuery).

Experience in monitoring, logging, and alerting operations for data workflows.

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.

Build AI where the work actually happens.

Celeco works inside real businesses to understand critical workflows, ship production AI systems and stay through adoption.

We are hiring our first Intelligence Architect (Forward-Deployed AI Engineer).

⌁

Full-time · Remote-first · Optional hybrid in Bengaluru

At least one year of professional engineering experience. Customer travel when the work requires it.

The role

An Intelligence Architect enters a customer environment with an unfinished question and leaves behind a working, measurable system.

This is a hands-on engineering role at the boundary of product, operations and customer delivery. You will learn the domain, inspect the existing systems and data, decide what should be built, and write the code that puts it into production.

What you will own

• Interview and shadow the people doing the work, then map the real workflow, including hand-offs, exceptions and workarounds.
• Understand the customer's application stack, APIs, data, identity model, security constraints and deployment environment.
• Turn business goals into a technical scope, system design, delivery plan and measures of success.
• Build across the stack: AI workflows, data pipelines, integrations, backend services and the interfaces people use.
• Choose the simplest reliable approach. The answer may combine agents, retrieval, rules and conventional software.
• Create evals from representative cases, define quality and failure metrics, inspect traces, red-team the system and set launch thresholds.
• Make practical trade-offs across accuracy, latency, cost, privacy, security and speed.
• Take prototypes into production with tests, monitoring, access controls, documentation and a plan for failure and recovery.
• Work beside customer teams during rollout and improve the system until it becomes part of the workflow.
• Turn what works into reusable components, evaluation sets and playbooks for future Celeco deployments.

You will probably thrive here if

• You have at least one year of professional software or product engineering experience.
• You have shipped a real system used by other people and can explain what you owned, what broke and what you changed.
• You are strong in Python or TypeScript and comfortable moving across unfamiliar codebases, APIs, databases and cloud services.
• You have built with language or multimodal models and understand prompting, structured outputs, retrieval, tool use and model failure modes.
• You use Cursor, Codex, Claude Code or similar tools as part of your engineering workflow, while still reviewing, testing and understanding the code you ship.
• You can create an evaluation set, choose useful quality metrics and improve a system through error analysis instead of prompt guesswork.
• You can speak with an operator, an engineering team and a senior leader without losing the thread of the problem.
• You work well with incomplete requirements, write clearly and surface risks early.
• You care about whether people use what you build and whether it changes a business outcome.
• You can commit full-time and travel to customer sites when discovery or rollout is better done in person.

Experience with cloud deployment, containers, CI/CD, observability, enterprise integrations, authentication or security is useful. We do not expect one person to arrive knowing every framework, cloud or industry.

What you will get

• Direct ownership of live customer problems from discovery through production.
• Close collaboration with Celeco's founders and customer leadership teams.
• Exposure to different industries, operating models and technical environments.
• The freedom to choose the technical approach and the responsibility to prove that it works.
• A role in defining Celeco's engineering methods, reusable systems and technical culture while the company is early.
• A remote-first setup, with the option to work together in Bengaluru.

If the work sounds like you but your background is unconventional, apply. We care more about what you have built, how you think and how quickly you learn than pedigree.

Role Summary:

We are looking for a Forward Deployed Engineer with strong hands-on experience in Databricks and Generative AI/Claude to work closely with clients, business stakeholders, and internal engineering teams. The ideal candidate will combine strong Data Engineering, Software Engineering, Databricks, and Generative AI skills with the ability to understand business problems and rapidly build, deploy, and optimize production-ready solutions. This is a client-facing, hands-on engineering role where you will work from problem discovery and solution design through POC development, production deployment, and ongoing optimization.

Key Responsibilities:

Forward Deployed Engineering

• Work directly with clients and stakeholders to understand business and technical requirements.
• Translate business problems into scalable data, AI, and software solutions.
• Design and develop POCs and rapidly validate technical solutions.
• Convert successful POCs into reliable, production-ready applications.
• Work closely with client engineering and data teams during implementation and deployment.
• Troubleshoot production issues and continuously optimize deployed solutions.
• Act as a technical bridge between clients, delivery teams, data engineers, AI engineers, and architects.

Databricks & Data Engineering

• Design and develop scalable data solutions using Databricks, PySpark, Python, and SQL.
• Build and optimize data ingestion, transformation, and ETL/ELT pipelines.
• Work with Databricks Lakehouse, Delta Lake, and Unity Catalog.
• Develop Databricks Workflows and production data pipelines.
• Implement data processing solutions for structured and semi-structured datasets.
• Optimize Databricks workloads for performance, scalability, reliability, and cost.
• Integrate Databricks with databases, APIs, cloud platforms, and enterprise applications.

Generative AI & Claude

• Build enterprise AI solutions using Claude and other Large Language Models (LLMs).
• Integrate Claude APIs into applications and business workflows.
• Develop RAG (Retrieval-Augmented Generation) solutions using enterprise data.
• Work with embeddings, vector search, semantic search, and knowledge retrieval.
• Develop AI-powered applications for summarization, classification, information extraction, question answering, and document processing.
• Implement prompt engineering, structured outputs, tool/function calling, and context management.
• Develop and integrate AI agents and multi-step AI workflows where applicable.
• Evaluate LLM responses for accuracy, relevance, groundedness, latency, and cost.
• Implement appropriate AI guardrails, security, and data privacy controls.

Production & Deployment

• Deploy AI and data solutions into production environments.
• Work with APIs, microservices, Git, CI/CD, containers, and cloud platforms.
• Monitor application and pipeline performance and troubleshoot issues.
• Collaborate with Data Scientists and ML Engineers to productionize AI/ML models.
• Ensure solutions meet security, scalability, reliability, and maintainability requirements.

Required Skills & Experience

• 4+ years of experience in Data Engineering, Software Engineering, AI/ML Engineering, or a related field.
• Strong hands-on experience with Databricks.
• Strong proficiency in Python, PySpark, and SQL.
• Experience with Delta Lake and Lakehouse Architecture.
• Experience working with Generative AI / LLMs.
• Hands-on experience with Claude / Anthropic APIs is preferred.
• Experience with RAG, embeddings, vector databases, and semantic search.
• Strong understanding of REST APIs and enterprise integrations.
• Experience developing production-grade applications and data pipelines.
• Strong problem-solving and troubleshooting capabilities.
• Excellent communication and client-facing skills.

Preferred Skills

• Experience with Claude Code / Anthropic ecosystem.
• Experience with OpenAI, Azure OpenAI, AWS Bedrock, or other LLM platforms.
• Experience with LangChain, LangGraph, LlamaIndex, or equivalent frameworks.
• Experience with Databricks Unity Catalog, Workflows, and MLflow.
• Experience with AWS, Azure, or GCP.
• Experience with Docker, Kubernetes, and CI/CD.
• Exposure to AI agents and agentic workflows.
• Knowledge of AI evaluation, guardrails, security, and responsible AI.
• Experience working in consulting, client delivery, or customer-facing engineering environments.

Key Competencies

• Strong customer-facing and stakeholder management skills.
• Ability to understand ambiguous business problems and translate them into technical solutions.
• Strong ownership and execution mindset.
• Ability to rapidly prototype, iterate, and productionize solutions.
• Strong analytical and troubleshooting skills.
• Comfortable working in fast-paced and dynamic client environments.
• Excellent written and verbal communication.
• Ability to work independently as well as collaboratively with distributed teams.