PASSION - AS A WAY OF LIFE and culture

We are looking for a full stack consultant to join our dynamic team! In this role, you will work on owning, designing and building highly scalable applications. You will work with a team of AI, DevOps, Frontend and Data Engineers.  If you enjoy working across both Backend and Frontend and thrive in a fast-paced environment, we want to hear from you!

What we need (Please read carefully before you apply)

• Own and Develop highly scalable applications - MicroServices, Analytics backends and Frontends
• Design and build Microservices for large backends.
• Work with MongoDb, Redis, Graphs and Timeseries databases to manage data effectively.
• Good Knowledge in system design for large scale distributed systems - Must
• Collaborate with product managers, designers, and other developers to deliver seamless user experiences.
• Ensure cross-browser compatibility, responsive design, and high-performance web interfaces.
• Write clean, modular, and well-documented code following industry best practices.
• Participate in code reviews and provide constructive feedback to peers.
• Contributed to deployments using CI/CD pipelines for automated builds and releases.
• Troubleshoot, debug, and resolve production issues.

Technology Skills 

Backend:

• Proficiency in Node.js, Express.js, / Python / Fast API
• Experience building highly scalable Microservices or GraphQL Backends.
• Experience in system design and fault tolerance - Kafka, DLQs, Circuit breakers etc.
• Experience in Data Crunching, Data Lakes and Analytics

Frontend (Good to have):

• Strong knowledge of JavaScript, HTML5, CSS3.
• Experience with React.js / Nextjs / Vuejs
• Familiarity with frontend tooling (Vite,Webpack, lint, etc)

Databases:

• Hands-on experience with MongoDB, InfluxDb, Sql Dbs, Redis and Key/value stores. 
• Knowledge of Elasticsearch is a plus.

Cloud & DevOps:

• Experience with AWS, Azure, or Google Cloud Platform.
• Experience with CI/CD pipelines (GitHub Actions, Jenkins, etc.).
• Familiarity with Docker and Kubernetes.

Version Control:

• Expertise with Git and GitHub/GitLab workflows.

Soft Skills:

• Strong problem-solving skills with a focus on scalable solutions.
• Excellent communication and collaboration abilities.
• Ability to thrive in an Agile/Scrum environment.

What to expect

• Develop Rockstar full stack engineer problem solving with exposure to wide variety of problems 
•  Fun loving and hard working team !
•  Vibrant startup culture
•  Passion for consulting (not just an engineering role)

We are looking for a distributed systems consultant to join our dynamic team! In this role, you will work on owning, designing and building highly scalable backend systems. You will work with a team of Fullstack, AI, DevOps, Frontend and Data Engineers.  If you enjoy working across both Backend and Frontend and thrive in a fast-paced environment, we want to hear from you!

What we need (Please read carefully before you apply)

• Own and Develop highly scalable distributed applications - MicroServices, Analytics backends, Service Meshes, Gateways, Message Brokers, TmeSeries databases, IOT Backends
• Very Good System Design expertise and ability to build Microservices for large backends.
• Work with Relational, NoSQL, Graphs and Timeseries databases to manage data effectively.
• Experience and knowledge in building highly reliable and fault tolerant system design mechanisms including but not limited to Message Queues, DLQs, Circuit breakers, 
• Design and own scalable, fault-tolerant services and APIs (REST/GraphQL/gRPC).
• Translate product requirements into domain models, service boundaries, and data contracts.
• Choose storage and indexing strategies (RDBMS/NoSQL/Time-series/Search) with clear trade-offs.
• Define SLAs/SLOs/SLIs; capacity plan for peak, growth, and failover.
• Produce architecture docs (C4/sequence diagrams), ADRs, and threat models.

Scalability & Reliability

• Caching patterns (cache-aside, read-through, write-behind, invalidation strategies).
• Async systems with queues/streams (Kafka/RabbitMQ/SQS), backpressure, idempotency, DLQs.
• Rate limiting, load shedding, bulkheads, circuit breakers, retries with jitter.
• Blue-green/canary/shadow releases; zero-downtime migrations.

Data Modeling & Consistency

• Event-driven designs, outbox/inbox patterns, CDC, exactly-once via at-least-once + idempotency.
• CQRS + Event Sourcing when appropriate; read models/materialized views.
• Transaction strategies: single-DB ACID vs Sagas/TCC for distributed workflows.
• Indexing, query planning, and data lifecycle (TTL, archiving, GDPR/PII handling).

API & Integration Design

• Versioning strategies, pagination, filtering, partial responses.
• Authentication/authorization (OAuth2/OIDC, mTLS, service-to-service tokens).
• Schema evolution (Protobuf/Avro/OpenAPI), backward/forward compatibility.
• Webhooks and Graph change notifications; webhook security and replay handling.

Observability & Operations

• Structured logging, metrics, and distributed tracing (OpenTelemetry).
• Health checks (startup/readiness/liveness), SLIs for latency, error rate, saturation.
• Runbooks, SLO error budgets, incident response, postmortems.

Performance & Cost

• Profiling (CPU/memory/I/O), latency budgets, p99 tuning, connection pooling.
• Storage and egress cost awareness, cache hit-rate targets, multi-AZ/region trade-offs.
• FinOps basics: cost per request/job, right-sizing, autoscaling policies.

Security & Compliance

• Secrets management (KMS/Vault), key rotation, least-privilege IAM.
• Threat modeling (OWASP ASVS), input validation, encryption at rest/in transit.
• Audit trails, tamper-evident logs, compliance considerations (GDPR/PCI where relevant).

Testing & Delivery

• Test strategy: unit, contract, component, integration, load/chaos tests.
• CI/CD with gated deploys, infra as code (Terraform/CloudFormation), GitOps.
• Schema/data migration workflows; feature flags and kill-switches.

Cloud & Runtime

• Containerization and orchestration (Docker/Kubernetes), service mesh basics.
• Storage choices (Postgres/MySQL, DynamoDB/Cassandra, Redis/Memcached, Elastic/OpenSearch).
• Files/objects (S3/GCS), CDN usage, object lifecycle, pre-signed URLs.

Nice-to-Have / Senior Signals

• Prior ownership of a multi-service domain or high-throughput pipeline.
• Experience with multi-tenant architectures and isolation strategies.
• Designed multi-region active-active or disaster recovery with RTO/RPO targets.
• Experience in Data Crunching, Data Lakes and Analytics
• Good Knowledge in system design for large scale distributed systems - Kafka
• Collaborate with product managers, designers, and other developers to deliver seamless user experiences.
• Write clean, modular, and well-documented code following industry best practices.
• Perform code reviews and provide constructive feedback to peers.
• Contributed to deployments using CI/CD pipelines for automated builds and releases.
• Troubleshoot, debug, and resolve production issues.

Frontend (Good to have):

• Strong knowledge of JavaScript, HTML5, CSS3.
• Familiarity with React.js / Nextjs 
• Familiarity with frontend tooling (Vite,Webpack, lint, etc)

Soft Skills:

• Strong problem-solving skills with a focus on scalable solutions.
• Excellent communication and collaboration abilities.
• Ability to thrive in an Agile/Scrum environment.

What to expect

• Develop ***Rockstar*** status with problem solving with exposure to wide variety of problems 
•  Fun loving and hard working team !
•  Vibrant startup culture
•  Passion for consulting (not just an engineering role)

We are looking for an experienced AI Consultant to join our dynamic team! 

WHAT WE NEED (READ CAREFULLY BEFORE APPLYING)

• We are seeking a Senior AI Consultant with strong hands-on expertise in end-to-end AI solution development and the ability to guide cross-functional teams through the design, experimentation, and deployment of intelligent systems.
• The ideal candidate is equally comfortable in architecting solutions, writing production-grade code, mentoring engineers, and working directly with customers to translate business challenges into AI implementations that deliver measurable impact.

KEY RESPONSIBILITIES

• Lead the design and development of AI systems spanning LLMs, Computer Vision, Agentic AI, and ASR/NLP pipelines.
   
• Drive model experimentation, fine-tuning, and evaluation using open-source and proprietary models (e.g., Llama 3, Gemini, NLLB-200, Whisper, etc.).
   
• Architect context-aware and retrieval-augmented (RAG) frameworks integrating vector, time-series, and graph databases.
   
• Build and optimize multi-agent frameworks for reasoning, orchestration, and autonomous task execution.
   
• Apply context engineering principles—prompt optimization, memory design, and grounding—to improve model reliability.
   
• Design distributed inference and training pipelines (vLLM, Ray, Triton, Kubernetes) for scalable deployments.
   
• Implement video analytics and computer vision solutions using frameworks like OpenCV, PyTorch, TensorRT, and ONNX.
   
• Guide teams in data engineering, feature extraction, and pipeline automation for multimodal datasets.
   
• Define evaluation frameworks (quality metrics, latency, cost per token, scalability) and monitor system performance.
   
• Collaborate with leadership to formulate AI roadmaps, solution architectures, and proof-of-concepts across domains.
   
• Coach and mentor engineers in AI development best practices, MLOps workflows, and experimentation discipline.

REQUIRED SKILLS AND EXPERIENCE

• 3–5 years of hands-on experience in AI/ML engineering, with exposure to production-grade systems.
• Strong in Python (and optionally C++/Rust/Go for performance components). 
• Proficiency in PyTorch, Transformers, LangChain, vLLM, or equivalent LLM frameworks.
• Deep understanding of LLM fine-tuning, embeddings, RAG, and context management strategies.
• Knowledge of Agentic AI architectures, multi-agent orchestration, and tool-use frameworks.
• Hands-on with vector databases (Milvus, Pinecone, FAISS), time-series DBs (InfluxDB, Timescale), and graph DBs (Neo4j, ArangoDB).
• Familiarity with Knowledge Graph construction, entity extraction, and ontology design.
• Experience in Computer Vision (object detection, segmentation, multimodal fusion) and Video AI pipelines 
• Experience with ASR and Speech-to-Text models (Gemini, Whisper, NLLB-200, Deepgram, etc.).
• Strong grasp of distributed systems concepts, GPU optimization, and containerized inference (Docker, Kubernetes).
• Knowledge of modern MLOps stacks (Weights & Biases, MLflow, Airflow, Ray Serve).
• Experience with hybrid cloud/on-prem GPU clusters, FinOps, and AI infrastructure scaling.
• Understanding of data privacy, prompt safety, and governance frameworks for enterprise AI deployments.

EXPERIENCE 

• Strong background in benchmarking, evaluation, and model diagnostics.
• Excellent communication and documentation skills to articulate architecture decisions and trade-offs.
• Ability to mentor and elevate AI teams through reviews, pair programming, and architectural guidance.
• Analytical thinker with a product mindset—able to convert business problems into measurable AI opportunities.
• Curiosity to explore emerging models, frameworks, and techniques and evaluate their enterprise applicability.
• Self-driven, detail-oriented, and passionate about elegant, maintainable code.
• Excellent communicator with an ability to translate technical goals into clear implementation paths.
• Collaborative mindset for pair-programming, mentoring, and cross-team integration.

WHAT TO EXPECT

•  Fun loving and hard working team !
•  Vibrant startup culture
•  Passion for consulting (not just an engineering role)