Requirements:
8+ years of fullstack development experience with strong skills in TypeScript/JavaScript, React, and Python (or Node/Go for backend).
Solid understanding of LLM APIs, agent frameworks (e.g., LangChain, AutoGPT, CrewAI), or custom AI pipelines- Advantage
Experience with modern cloud infrastructure (e.g., AWS, GCP, Docker, CI/CD).
Familiarity with vector databases (e.g., Pinecone, Weaviate, FAISS) and retrieval-augmented generation (RAG)- Advantage
Product-oriented mindset: you care deeply about building things that work well for users.
Bonus: experience with observability, feedback loops for AI agents, or embedded AI evaluation techniques.

Requirements:
5+ years of hands‑on software engineering experience building production systems at scale.
Strong proficiency in Python, with Practical knowledge of databases.
Strong grounding of LLM/AI application patterns (RAG, tool use, function calling, guardrails) and vendor APIs (OpenAI or similar).
Experience with vector store (pgvector, Pinecone, OpenSearch), feature/semantic layers, or retrieval pipelines
Familiarity with: eval frameworks, prompt/version management, offline/online A/B testing, and cost/latency optimization.
Clear written and verbal communication; able to drive alignment with concise design docs and reviews.
Nice to have:
Experience building developer platforms or internal tooling
Hands-on experience with model optimization, fine-tuning, or distillation techniques.
Deep experience with cloud infrastructure (AWS), containers (Docker, Kubernetes), and distributed systems.
Frontend development frameworks such as React.
Background in SaaS/enterprise environments with compliance requirements (SOC2, GDPR).

Requirements:
Technical Expertise:
Programming: Advanced proficiency in Python for backend development and AI applications
GenAI Mastery: Deep understanding of large language models (LLMs) and experience with major model APIs (OpenAI, Anthropic, Google, etc.)
Multi-Agent Systems: Expertise in designing and implementing GenAI multi-agent architectures
Prompt Engineering: Advanced skills in prompt design, optimization, and engineering techniques
Cloud Platforms:
Required: Hands-on experience with AI services in at least one major cloud platform (GCP, Azure, or AWS)
Advantage: Experience across multiple cloud platforms (AI Search, Vertex AI, SageMaker, etc.)
Development Frameworks: Experience with GenAI frameworks like LangChain and cloud-based retrieval services
Software Engineering: Strong background in microservices architecture, API development, and production system design
AI/ML Fundamentals: Solid understanding of deep learning principles and GenAI techniques
Containerization (Advantage): Experience with Docker and Kubernetes for deployment and orchestration
OCR Technologies (Advantage): Experience with Optical Character Recognition systems and document processing
Data Pipelines (Advantage): Experience building and maintaining data processing pipelines
Professional Experience
Mid+ Level Experience: 2+ years in AI/ML development with significant GenAI project experience
Production Systems: Proven track record of deploying and maintaining AI solutions in production environments
Client-Facing Experience: Comfortable with technical presentations and requirement gathering sessions
Education & Background
Preferred: Bachelor's or Master's degree in Computer Science, AI, Machine Learning, or related technical field
Alternative: Demonstrated industrial experience in developing deep learning and GenAI solutions (degree not required with strong portfolio)
Soft Skills
Problem-Solving: Excellent analytical and creative problem-solving abilities
Communication: Strong technical communication skills for both technical and non-technical audiences
Collaboration: Proven ability to work effectively in cross-functional teams
Adaptability: Thrives in fast-paced environments and eager to learn emerging technologies
Consulting Mindset: Ability to understand client needs and provide strategic technical guidance.

Requirements:
Requirements:

3+ years of hands-on experience in software engineering, with at least 1-2 years working on generative AI projects (LLMs, diffusion models, multimodal models, etc.).

Proven ability to go from idea to production-ideally with examples of real-world AI features youve shipped.

Fluency in Python, Node.js, or similar languages used in ML and full-stack development.

Experience with prompt engineering, fine-tuning, or embedding models using frameworks like LangChain, LlamaIndex, or similar.

Familiarity with AWS services and best practices, including Lambda, S3, SageMaker, ECS/EKS, Bedrock etc.

Experience with MLOps and model deployment practices (e.g., containerization, GPU inference, vector databases).

Creativity and initiative-able to pitch and prototype ideas with minimal oversight.

Strong communication skills and the ability to explain technical concepts to non-technical stakeholders.

Nice-to-Haves:

Prior experience integrating generative AI in FinOps or cloud cost optimization tools.

Background in NLP, computer vision, or other relevant ML fields.

Contributions to open-source AI tools or research.

Knowledge of responsible AI principles and handling model risks.

Requirements:
Expreince leading engineering teams in fast-paced environments with strong ownership and architectural responsibility.
Backend Expertise: 6+ years of backend development experience with deep expertise in Node.js and microservices. Strong distributed systems and API design experience.
GenAI Systems Experience: Hands-on experience building production LLM systems. Proven experience with RAG architectures, vector databases, embedding pipelines, and prompt orchestration. Experience designing multi-step or agentic AI workflows.
Infrastructure: Strong experience with AWS and Kubernetes in production environments. Deep knowledge of SQL & NoSQL systems.
Communication: Ability to translate complex AI systems into clear product and business decisions.

Requirements:
What we're looking for:

6+ years of backend engineering experience, with strong proficiency in more than one major programming language (such as Python, Java, Go, Rust, or Kotlin).
Solid understanding of AI systems architecture and experience working in environments involving AI agents, LLMs, or inference pipelines.
Proven experience in building and scaling backend APIs, microservices, and background jobs.
Strong experience with relational and NoSQL databases (e.g., PostgreSQL, MySQL, MongoDB, Redis), including schema design, data modeling, and query optimization.
Experience with message brokers and distributed systems (e.g., Pub/Sub, Redis Streams, RabbitMQ).
Highly adaptable, a strong team player, with the ability to quickly learn and apply new technologies.

Advantages:

Strong proficiency in Python and/or Node.js/NestJS.
Experience with clinical systems and healthcare data standards (FHIR, EMR/EHR).
Hands-on experience with AI agent frameworks such as LangChain, LangGraph, or LiteLLM.
Experience orchestrating hybrid model stacks combining commercial APIs (OpenAI, Google Gemini) with self-hosted or fine-tuned LLMs.
Familiarity with observability tools for AI systems (Langfuse, OpenTelemetry).

Requirements:
5+ years of experience in software engineering, with strong proficiency in Python.
Experience with LangGraph, LangChain, or similar frameworks for building AI agents.
Strong understanding of LLMs, prompt engineering, and AI agent architectures.
Familiarity with REST APIs and integrating external data sources.
Preferred Skills:
Familiarity with the AI Agents ecosystem, including different LLM providers, vector store solutions, MCPs, and more.
Experience with evaluating LLM performance and quality.
Familiarity with cloud environments, especially AWS.

Requirements:
Technical Expertise
Advanced proficiency in Python for backend development and AI systems Deep understanding of large language models and generative AI techniques Hands-on experience designing and implementing multi-agent architectures Advanced prompt engineering and orchestration strategies Strong background in microservices architecture, API development, and production system design Hands-on experience with at least one major cloud platform (GCP, Azure, or AWS)
Professional Experience
2-3+ years of experience in AI/ML development with significant GenAI project exposure Proven experience deploying and maintaining AI systems in production Client-facing experience in technical consulting or solution delivery roles
Advantages
Hands-on experience developing directly against LLM provider SDKs and APIs (e.g., OpenAI, Anthropic, Google), including tool/function calling, streaming, and advanced orchestration patterns. Docker and Kubernetes experience OCR systems and document intelligence experience Data pipeline development and maintenance experience
Education & Background
Bachelors or Masters degree in Computer Science, AI, Machine Learning, or related field (or equivalent demonstrated industry experience)
Soft Skills
Strong problem-solving and analytical capabilities Excellent technical communication skills Ability to collaborate effectively across teams Adaptability in fast-paced, evolving technical environments Consulting mindset with strong client focus.

Requirements:
Overall of 5 years of hands-on experience (3 years automation, 2 years in GenAI)
Hands-on experience building AI-powered workflows or agents
Strong understanding of LLMs and prompt engineering
Experience with API-based integrations and SaaS systems
Familiarity with automation platforms and orchestration tools
Experience with agentic patterns (planner-executor, tool-using agents, multi-agent setups)
Experience implementing human-in-the-loop and fallback mechanisms
Understanding of AI limitations, bias, and reliability concerns
Strong problem-solving and systems-thinking mindset
Ability to work independently and drive initiatives end-to-end
Excellent communication with both technical and non-technical stakeholders.

Requirements:
What youll need:
Excellent coding skills in Python/TypeScript, with at least 5 years of hands-on experience building reliable backend services, agents and tooling. Familiarity with modern ML frameworks (e.g., PyTorch, TensorFlow, Hugging Face) is a strong advantage.
Experience designing, deploying, and maintaining production systems that integrate ML components, including APIs, microservices, model serving layers, feature pipelines, monitoring, and CI/CD/MLOps workflows.
Solid experience with AI related contexts Understanding of prompt engineering and LLM optimization techniques, RAG architecture
Solid understanding of distributed systems concepts, performance optimization, observability, and operating services at scale.
Strong communication skills, with the ability to bridge technical, product, and business perspectives.
Prior experience in cybersecurity, fraud prevention, or identity management is a plus, especially with secure system architectures or ML-augmented decisioning systems.
Advantages:
Experience integrating with LLM APIs (OpenAI, Anthropic Claude, etc.)
Experience with agent frameworks (LangChain, LlamaIndex, AutoGPT)
Background in ML/AI concepts and model deployment
Experience with message queues (RabbitMQ, Kafka) and event-driven architectures
Experience with function calling and tool use patterns in LLMs.