• Contribute to the prototyping and deployment of AI and Generative AI solutions to support client pursuits, sector use cases and demonstrations. • Develop and test LLM-powered applications and agentic workflows under guidance, including tool or function calling patterns. • Support engineering of data-driven architectures across cloud and on-premises environments. • Assist in developing scalable AI systems with embedded Responsible AI and security controls. • Apply MLOps and LLMOps practices including experimentation tracking, model testing and deployment support. • Build and maintain data pipelines integrating structured and unstructured data sources. • Work with vector databases alongside relational and NoSQL systems. • Support deployment of AI workloads using platforms such as AWS and Azure. • Collaborate with architects, sector teams and engineers to transition proof-of-concepts toward production-ready solutions.

Role Description As a Senior Software Engineer on our Enterprise Retrieval team, you’ll help build the retrieval layer that powers enterprise AI agents at Workato. Your work will let an agent answer “what’s the status of the Acme renewal?” by stitching together a Salesforce opportunity, a call summary, the latest Zendesk ticket, a Jira blocker, and a SharePoint contract — all in one ranked, permission-aware response. This is the kind of problem where classical Information Retrieval, dense vector retrieval, knowledge graphs, and LLM-driven reasoning all collide. You’ll work across heterogeneous content (docs, tickets, tasks, CRM records, call transcripts, chat threads), heterogeneous permissions (every source has its own ACL model), and very real freshness constraints (yesterday’s answer is often wrong). It’s a hands-on Senior IC role for someone who wants to go deep on retrieval quality and see their work directly shape how thousands of enterprises put AI agents to work. In this role, you will also be responsible to: - Build a unified retrieval layer across enterprise systems — Google Drive, SharePoint, Confluence, Jira, Asana, Zendesk, Freshdesk, Salesforce, Notion, and more — exposing a clean, agent-friendly interface. - Design hybrid retrieval pipelines that combine lexical (BM25), dense vector, and structured (SQL/graph) retrieval, with smart re-ranking tuned for cross-source results. - Engineer ingestion and freshness pipelines that incrementally sync millions of documents, tickets, tasks, and CRM records with low end-to-end latency and predictable cost. - Own permission-aware retrieval (ACL preservation) — make sure the engine never returns a document a user (or their agent) isn’t entitled to see, mirroring source-system permissions exactly. - Build query understanding for agents — intent parsing, entity linking across systems (a “customer” in Salesforce is the same as in Zendesk), and LLM-assisted query rewriting and decomposition. - Design chunking and embedding strategies tailored to each content type — long docs, short tickets, threaded conversations, structured records, call transcripts. - Build evaluation and experimentation harnesses (NDCG, MRR, recall@k, faithfulness, citation accuracy) for both retrieval and end-to-end agent answers. - Ship production-grade, observable systems with strong SLOs on latency, freshness, recall, and cost — and the dashboards/tracing to prove it. - Mentor teammates and raise the bar on retrieval architecture, evaluation rigor, and engineering craft. Qualifications - 3-5 years building production search, retrieval, knowledge-base, or recommendation systems. - Strong proficiency in at least one modern backend language — Python, Go, Java, or similar. - Hands-on experience with search engines such as OpenSearch, Elasticsearch, Solr, or Vespa, including index design and analyzers. - Solid grounding in IR fundamentals: TF-IDF, BM25, learning-to-rank, query parsing, and relevance evaluation. - Working experience with vector search and embeddings — FAISS, pgvector, Pinecone, Weaviate, Qdrant, Milvus, or native Elasticsearch/OpenSearch kNN. - Experience designing or contributing to RAG pipelines and semantic search systems in production. - Familiarity with modern NLP/LLM tooling: transformer embeddings, cross-encoder re-rankers, prompt engineering, and frameworks like LangChain, LlamaIndex, or Haystack. - Comfortable building integrations against SaaS APIs (REST/GraphQL/webhooks), handling OAuth, rate limits, pagination, and incremental sync. - Solid intuition for ACL/permission models in enterprise systems (Drive sharing, SharePoint groups, Jira project roles, Salesforce sharing rules, etc.) and how to preserve them in a retrieval layer. - Strong SQL skills, comfort with NoSQL/document stores, and experience with large-scale distributed systems. - Familiarity with cloud platforms (AWS, GCP, or Azure), containerization, and CI/CD. Requirements - Clear communicator who can explain technical trade-offs to engineers, PMs, and executives alike. - Collaborative — you partner naturally with ML, product, security, and platform teams. - Quality-obsessed and detail-oriented, with an instinct for measurable outcomes over vibes. - Self-directed; comfortable taking an ambiguous problem from zero to shipped. - Genuinely curious about the hard, interesting problems hiding inside enterprise retrieval — heterogeneity, permissions, freshness, and trust. Nice to Have - Experience with knowledge graphs, entity resolution, or cross-source identity linking. - Experience tuning or fine-tuning embedding models (sentence-transformers, BGE, E5, etc.) for domain-specific retrieval. - Exposure to agentic AI patterns — tool use, function calling, MCP, or multi-step retrieval planning. - Experience with streaming/real-time ingestion (Kafka, Flink, Spark) and cost optimization at scale. - Background in enterprise search, e-discovery, observability, or DLP — anywhere you’ve had to handle messy multi-source content with strict access controls. - Open-source contributions, published research, or writing on retrieval, IR, or applied ML.

• Design, implement, and own multi-step agentic flows using LangChain, LangGraph, and similar. • Build tools, agents, and chains that connect to live data sources (APIs, databases, time-series feeds) • Integrate LLMs (Claude, GPT, Gemini, etc') into reliable, observable pipelines • Own prompt engineering, context management, and output validation • Experience working in large-scale and big data environments • Deploy and monitor AI services on AWS or Azure • Instrument flows with logging and tracing so we can debug and improve them • Build React interfaces that surface AI outputs: summaries, alerts, recommendations, and interactive co-pilot features • Connect frontend components to streaming AI responses • Write clean, maintainable TypeScript code

Role Description SoftClouds LLC is looking for an experienced Senior AI Engineer with a strong background in Python, React, LangChain, LangGraph, AWD Bedrock, and Azure Foundry. The ideal candidate will have a proven track record in developing and deploying advanced AI solutions and a passion for driving innovation in the field. - Design, develop, and implement GEN AI products, AgentiC AI, predictive analytics, and algorithms using Python and related libraries. - Collaborate with cross-functional teams to integrate AI solutions into existing systems using React and LangChain. - Utilize LangGraph for efficient data processing and analysis. - Leverage AWD Bedrock and Azure Foundry for scalable and reliable AI solution deployment. - Conduct research and stay up-to-date with the latest advancements in AI technologies. - Troubleshoot and optimize AI models to improve performance and accuracy. - Mentor and guide junior engineers in best practices and advanced AI techniques. - Implement algorithms for processing, analyzing, and understanding textual data, ensuring high performance and scalability. - Focus on essential tasks like data manipulation, regular expressions, and data parsing using Python libraries such as Spacy, LlamaIndex, LangChain, and Regex. - Integrate with pre-built NLP models, ensuring efficient model usage for tasks like Text Generation, entity recognition, text classification, and text summarization. - Write reusable Python code following best practices for quality, modularity, and performance. - Collaborate with teams to gather requirements and deliver Python-based solutions for business needs. - Optimize Python code to ensure efficiency, scalability, and minimal resource usage in production. - Use Git for version control, managing Python code updates, and collaborating with team members. - Research new libraries and tools to improve Python-based systems and workflows. - Solve complex problems efficiently, adapting to new challenges and evolving requirements. - Maintain clear documentation for all Python applications, ensuring clarity and ease of use. Qualifications - Extensive experience in Python programming and AI development, working with various Python libraries and tools. - Proficient in libraries like TensorFlow, PyTorch, spaCy, NLTK, and Hugging Face to build, train, and fine-tune NLP models for tasks and React for front-end development. - Hands-on experience with LangChain and LangGraph. - Familiarity with AWD Bedrock and Azure Foundry for AI deployment. - Strong problem-solving skills and the ability to work collaboratively in a team environment. - Experience with large datasets, handling, processing, and optimizing data workflows in Python. - Strong understanding of software development processes and methodologies like Agile and Git. - Write clean, modular, reusable code with focus on quality, scalability, and performance. - Experience using version control for collaborative development and code management. - Excellent communication skills for explaining complex technical concepts to both technical and non-technical teams. - Detail-oriented with a focus on writing error-free, high-quality Python code. - Adapt quickly to new technologies and take ownership of learning opportunities. - Knowledge and experience of working with Large Language Models specifically OpenAI Models. - Knowledge and experience of working with OpenSearch/Elasticsearch. - Knowledge/experience in Vector Database, Vector Search, Retrieval Augmented Generation (RAG), Chunking and Splitting. - Knowledge and experience in designing various prompts (Prompt Engineering). - Excellent communication skills and the ability to explain complex AI concepts to non-technical stakeholders.